diff --git a/cleanroom/datasets/preprocessor_time.py b/cleanroom/datasets/preprocessor_time.py
new file mode 100644
index 0000000000000000000000000000000000000000..031324af56bbd6b7ccc4e9844c88b2bd16fec164
--- /dev/null
+++ b/cleanroom/datasets/preprocessor_time.py
@@ -0,0 +1,347 @@
+
+import pandas as pd
+import torch
+import h5py
+import json
+from typing import Dict, Set, List, Tuple
+from collections import defaultdict
+import re
+from io import StringIO
+
+# --- Helper Functions ---
+
+def load_boundary_nodes_from_csvs(boundary_csv_folder_path: str, target_column_name: str) -> Dict[str, Set[int]]:
+    # This function appears correct and has been kept as is.
+    # ... (function content is unchanged) ...
+    boundary_nodes_map: Dict[str, Set[int]] = {}
+    all_seen_nodes: Set[int] = set()
+    walls_nodes: Set[int] = set()
+    everything_csv_path = None
+
+    print(f"Scanning for boundary CSV files in: {boundary_csv_folder_path}")
+
+    if not os.path.isdir(boundary_csv_folder_path):
+        print(f"Error: Folder not found at {boundary_csv_folder_path}")
+        return boundary_nodes_map
+
+    # Step 1: Process 'walls.csv' first
+    walls_file_path = os.path.join(boundary_csv_folder_path, "walls.csv")
+    if os.path.exists(walls_file_path):
+        print(f"\nProcessing 'walls.csv'...")
+        try:
+            df_walls = pd.read_csv(walls_file_path, skiprows=5, header=0, engine='python')
+            if target_column_name in df_walls.columns:
+                walls_nodes = set(pd.to_numeric(df_walls[target_column_name], errors='coerce').dropna().astype(int).tolist())
+                boundary_nodes_map['walls'] = walls_nodes
+                all_seen_nodes.update(walls_nodes)
+                print(f"  - Loaded {len(walls_nodes)} nodes for boundary type: 'walls'.")
+            else:
+                print(f"  - Warning: 'walls.csv' does not contain the expected column '{target_column_name}'. Skipping walls assignment.")
+        except pd.errors.EmptyDataError:
+            print(f"  - Warning: 'walls.csv' is empty after skipping rows. Skipping walls assignment.")
+        except Exception as e:
+            print(f"  - Error reading 'walls.csv': {e}. Skipping walls assignment.")
+    else:
+        print("\n'walls.csv' not found in the specified directory.")
+
+    # Step 2: Process all other CSVs
+    print("\nProcessing other boundary CSVs (excluding walls.csv and everything.csv)...")
+    for filename in os.listdir(boundary_csv_folder_path):
+        if filename.endswith(".csv") and filename not in ["walls.csv", "everything.csv"]:
+            file_path = os.path.join(boundary_csv_folder_path, filename)
+            boundary_type = os.path.splitext(filename)[0]
+            try:
+                df = pd.read_csv(file_path, skiprows=5, header=0, engine='python')
+                if target_column_name in df.columns:
+                    nodes_from_file = set(pd.to_numeric(df[target_column_name], errors='coerce').dropna().astype(int).tolist())
+                    filtered_nodes = nodes_from_file - walls_nodes
+                    if filtered_nodes:
+                        boundary_nodes_map[boundary_type] = filtered_nodes
+                        all_seen_nodes.update(filtered_nodes)
+                        print(f"  - Loaded {len(filtered_nodes)} filtered nodes for boundary type: '{boundary_type}'. (Original: {len(nodes_from_file)})")
+                else:
+                    print(f"  - Warning: '{filename}' does not contain '{target_column_name}'. Skipping.")
+            except Exception as e:
+                print(f"  - Error reading '{filename}': {e}. Skipping.")
+        elif filename == "everything.csv":
+            everything_csv_path = os.path.join(boundary_csv_folder_path, filename)
+
+    # Step 3: Process 'everything.csv'
+    if everything_csv_path:
+        print(f"\nProcessing 'everything.csv' for interior nodes...")
+        try:
+            df_everything = pd.read_csv(everything_csv_path, skiprows=5, header=0, engine='python')
+            if target_column_name in df_everything.columns:
+                all_nodes_in_everything = set(pd.to_numeric(df_everything[target_column_name], errors='coerce').dropna().astype(int).tolist())
+                interior_nodes = all_nodes_in_everything - all_seen_nodes
+                boundary_nodes_map['interior'] = interior_nodes
+                print(f"  - Loaded {len(interior_nodes)} unique interior nodes from 'everything.csv'.")
+            else:
+                print(f"  - Warning: 'everything.csv' does not contain '{target_column_name}'.")
+        except Exception as e:
+            print(f"  - Error reading 'everything.csv': {e}.")
+    else:
+        print("\n'everything.csv' not found.")
+
+    print("\nFinished loading boundary nodes from CSVs.")
+    return boundary_nodes_map
+
+def load_velocity_lookup(csv_path: str) -> Dict[str, float]:
+    velocity_lookup = {}
+    if not os.path.exists(csv_path):
+        print(f"Warning: Velocity lookup file not found at {csv_path}")
+        return velocity_lookup
+    try:
+        df = pd.read_csv(csv_path)
+        velocity_lookup = {row['Design Points'].strip(): float(row['Inlet velocity']) for _, row in df.iterrows()}
+        print(f"Loaded velocity lookup table with {len(velocity_lookup)} design points.")
+    except Exception as e:
+        print(f"Error loading velocity lookup from {csv_path}: {e}")
+    return velocity_lookup
+
+def get_design_point_info(folder_name: str, velocity_lookup: Dict[str, float]) -> Tuple[str, float]:
+    # <<< FIXED: Corrected regex to get the full DP name, e.g., 'DP1' >>>
+    dp_match = re.search(r'(DP\d+)', folder_name)
+    design_point = dp_match.group(1) if dp_match else 'DP_Unknown'
+    velocity = velocity_lookup.get(design_point, 0.0)
+    if velocity == 0.0 and design_point != 'DP_Unknown':
+        print(f"Warning: No velocity found for {design_point} in lookup table.")
+    return design_point, velocity
+
+
+# <<< FIXED: Rewritten to parse the new CSV format >>>
+def read_velocity_csv_file(csv_file_path: str) -> Tuple[List[int], List[List[float]], List[List[float]]]:
+    """
+    Reads a time-stepped velocity CSV file with the new column format.
+    """
+    print(f"  Reading file: {os.path.basename(csv_file_path)}")
+    try:
+        # The data starts after the '[Data]' line, so we skip until we find it.
+        with open(csv_file_path, 'r') as f:
+            lines = f.readlines()
+        
+        data_start_index = -1
+        for i, line in enumerate(lines):
+            if '[Data]' in line:
+                data_start_index = i + 1
+                break
+
+        if data_start_index == -1:
+            print("    Warning: '[Data]' section not found. Cannot read file.")
+            return [], [], []
+
+        # The line after '[Data]' is the header.
+        header_line = lines[data_start_index]
+        data_lines = lines[data_start_index + 1:]
+        
+        # Use pandas to parse the data from the identified lines
+        df = pd.read_csv(StringIO("".join(data_lines)), header=None, sep=',\s*', engine='python')
+        
+        # Assign column names based on the header line for clarity (optional but good practice)
+        df.columns = [h.strip() for h in header_line.split(',')]
+
+        # Select columns based on their position (0-indexed) as per your requirement
+        # X, Y, Z are columns 0, 1, 2
+        # Domain Node Number is column 3
+        # Velocity u, v, w are columns 5, 6, 7
+        coordinates = df.iloc[:, [0, 1, 2]].values.tolist()
+        nodes = df.iloc[:, 3].astype(int).tolist()
+        velocities = df.iloc[:, [5, 6, 7]].values.tolist()
+
+    except Exception as e:
+        print(f"  An error occurred while reading {csv_file_path}: {e}")
+        return [], [], []
+        
+    print(f"  Successfully read {len(nodes)} nodes.")
+    return nodes, coordinates, velocities
+
+def create_node_features(
+    velocity_node_ids: List[int],
+    boundary_nodes_map: Dict[str, Set[int]]
+) -> Tuple[torch.Tensor, Dict[str, int], Dict[str, str]]:
+    # This function appears correct and has been kept as is.
+    # ... (function content is unchanged) ...
+    all_node_types = sorted(list(boundary_nodes_map.keys()))
+    class_to_index = {name: i for i, name in enumerate(all_node_types)}
+    num_classes = len(all_node_types)
+    feature_vectors = []
+    node_type_counts = defaultdict(int)
+
+    for node_id in velocity_node_ids:
+        vector = [0] * num_classes
+        assigned_type = "interior" # Default
+        for boundary_type_name in all_node_types:
+            if node_id in boundary_nodes_map.get(boundary_type_name, set()):
+                assigned_type = boundary_type_name
+                break
+        
+        index = class_to_index[assigned_type]
+        vector[index] = 1
+        node_type_counts[assigned_type] += 1
+        feature_vectors.append(vector)
+    
+    final_zone_map = {name: name for name in all_node_types}
+    return torch.tensor(feature_vectors, dtype=torch.float32), dict(node_type_counts), final_zone_map
+
+def create_time_stepped_velocity_pairs(folder_path: str, boundary_nodes_map: Dict[str, Set[int]]) -> List[Dict]:
+    # This function appears correct and has been kept as is.
+    # ... (function content is unchanged) ...
+    print(f"  Creating time-stepped velocity pairs from: {folder_path}")
+    csv_files = sorted([f for f in os.listdir(folder_path) if f.endswith('.csv')])
+    
+    if len(csv_files) < 2:
+        print(f"    Need at least 2 time steps, found {len(csv_files)}")
+        return []
+    
+    trajectory_data = []
+    
+    for i in range(len(csv_files) - 1):
+        current_file = csv_files[i]
+        target_file = csv_files[i + 1]
+        
+        current_path = os.path.join(folder_path, current_file)
+        target_path = os.path.join(folder_path, target_file)
+        
+        print(f"    Processing time step {i+1}: {os.path.basename(current_file)} -> {os.path.basename(target_file)}")
+        
+        current_nodes, current_coordinates, current_velocities = read_velocity_csv_file(current_path)
+        target_nodes, _, target_velocities = read_velocity_csv_file(target_path)
+        
+        if not current_nodes or not target_nodes or current_nodes != target_nodes:
+            print(f"    Warning: Node mismatch or empty data. Skipping step.")
+            continue
+            
+        node_type_tensor, node_type_counts, zone_map = create_node_features(current_nodes, boundary_nodes_map)
+        
+        step_data = {
+            'step_number': i + 1, 'current_file': current_file, 'target_file': target_file,
+            'node_ids': current_nodes, 'coordinates': current_coordinates,
+            'current_velocities': current_velocities, 'target_velocities': target_velocities,
+            'node_type_tensor': node_type_tensor, 'node_type_counts': node_type_counts,
+            'num_nodes': len(current_nodes), 'zone_map': zone_map
+        }
+        trajectory_data.append(step_data)
+    
+    print(f"    Created {len(trajectory_data)} time-stepped velocity pairs")
+    return trajectory_data
+
+def save_data_to_h5(
+    base_folder_path: str,
+    design_point_range: range,
+    boundary_csv_folder_path: str,
+    output_folder_path: str,
+    velocity_lookup_csv_path: str
+):
+    device = torch.device('cpu')
+    os.makedirs(output_folder_path, exist_ok=True)
+
+    print("="*60 + "\nSTEP 1: LOADING VELOCITY LOOKUP TABLE\n" + "="*60)
+    velocity_lookup = load_velocity_lookup(velocity_lookup_csv_path)
+    
+    print("\n" + "="*60 + "\nSTEP 2: READING MESH BOUNDARY INFORMATION\n" + "="*60)
+    target_column_name = ' Domain Node Number [  ]' 
+    boundary_nodes_map = load_boundary_nodes_from_csvs(boundary_csv_folder_path, target_column_name)
+    if not boundary_nodes_map:
+        print("CRITICAL ERROR: Failed to read boundary nodes. Cannot proceed.")
+        return
+
+    basename = "final_data_timestep"
+    h5_path = os.path.join(output_folder_path, f"{basename}_data.h5")
+    meta_json_path = os.path.join(output_folder_path, f"{basename}_data.json")
+
+    print("\n" + "="*60 + "\nSTEP 3: PROCESSING TIME-STEPPED DATA\n" + "="*60)
+
+    group_to_folder_map = {}
+    final_class_map = {}
+    group_counter = 0
+    
+    with h5py.File(h5_path, 'w') as f:
+        for dp_num in design_point_range:
+            dp_folder = f"dataset_DP{dp_num}"
+            folder_path = os.path.join(base_folder_path, dp_folder)
+            
+            design_point, velocity = get_design_point_info(dp_folder, velocity_lookup)
+            
+            print(f"\n{'='*40}\nProcessing Design Point: {design_point} (Inlet Vel: {velocity} m/s)\n{'='*40}")
+            
+            if not os.path.exists(folder_path):
+                print(f"WARNING: Folder {folder_path} does not exist. Skipping...")
+                continue
+            
+            trajectory_data = create_time_stepped_velocity_pairs(folder_path, boundary_nodes_map)
+            
+            if not trajectory_data:
+                print(f"No valid trajectory data found in {dp_folder}")
+                continue
+            
+            if not final_class_map:
+                zone_map = trajectory_data[0].get('zone_map', {})
+                final_class_map = {name: i for i, name in enumerate(sorted(list(set(zone_map.values()))))}
+            
+            # Each DP folder's trajectory becomes one group in the H5 file
+            group_name = f"group_{group_counter}"
+            dataset_group = f.create_group(group_name)
+            
+            num_steps = len(trajectory_data)
+            num_nodes = trajectory_data[0]['num_nodes']
+            
+            # Stack all time steps into single arrays for this group
+            all_current_vel = torch.tensor([s['current_velocities'] for s in trajectory_data], dtype=torch.float32)
+            all_target_vel = torch.tensor([s['target_velocities'] for s in trajectory_data], dtype=torch.float32)
+            
+            # Static data (same for all steps in a trajectory)
+            coordinates = torch.tensor(trajectory_data[0]['coordinates'], dtype=torch.float32)
+            node_type = trajectory_data[0]['node_type_tensor']
+            
+            dataset_group.create_dataset("current_velocities", data=all_current_vel.cpu().numpy())
+            dataset_group.create_dataset("target_velocities", data=all_target_vel.cpu().numpy())
+            dataset_group.create_dataset("coordinates", data=coordinates.cpu().numpy())
+            dataset_group.create_dataset("node_type", data=node_type.cpu().numpy())
+            
+            group_to_folder_map[group_name] = {
+                'design_point': design_point, 'velocity': velocity,
+                'source_folder': dp_folder, 'num_steps': num_steps, 'num_nodes': num_nodes,
+                'node_type_counts': trajectory_data[0]['node_type_counts']
+            }
+            
+            print(f"  Saved {num_steps} time steps to H5 group: {group_name}")
+            group_counter += 1
+
+    metadata = {
+        "file_info": group_to_folder_map,
+        "class_map": final_class_map, # <<< FIXED: Use the populated class map
+        "total_groups": group_counter
+    }
+    
+    with open(meta_json_path, 'w') as json_file:
+        json.dump(metadata, json_file, indent=4)
+    
+    print("\n" + "="*60 + "\nPROCESSING COMPLETE\n" + "="*60)
+    print(f"Total groups created: {group_counter}")
+    print(f"H5 file saved to: {h5_path}")
+    print(f"Metadata saved to: {meta_json_path}")
+
+# --- Main Execution Block ---
+def main():
+    # --- CONFIGURATION ---
+    # Base folder path where all 'dataset_DPx' folders are located
+    BASE_FOLDER_PATH = r"C:\Users\accel\OneDrive\Desktop"
+    # Range of design points to process (e.g., range(1, 10) for DP1 to DP9)
+    DESIGN_POINT_RANGE = range(1, 10)
+    # Path to the DesignPoints.csv file for velocity lookup
+    VELOCITY_LOOKUP_PATH = r"d:\Python_and_machine_learning_tutorials\DesignPoints.csv"
+    # Path to the folder containing boundary definition CSVs
+    BOUNDARY_CSV_PATH = r"D:\data_validation\node_type_timestepped"
+    # Path where the final H5 and JSON files will be saved
+    OUTPUT_FOLDER_PATH = r"D:\ANK_official\a_Dataset_generated\preprocessed_for_model\timestep_data"
+    
+    save_data_to_h5(
+        BASE_FOLDER_PATH,
+        DESIGN_POINT_RANGE,
+        BOUNDARY_CSV_PATH,
+        OUTPUT_FOLDER_PATH,
+        VELOCITY_LOOKUP_PATH
+    )
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/cleanroom/datasets/time_pt.py b/cleanroom/datasets/time_pt.py
new file mode 100644
index 0000000000000000000000000000000000000000..23442e1e5bc218212574f2dcea11fda3224cb2a4
--- /dev/null
+++ b/cleanroom/datasets/time_pt.py
@@ -0,0 +1,133 @@
+import os
+import h5py
+import torch
+import json
+import numpy as np
+import random
+from typing import List, Dict, Any
+
+def create_pt_files_from_h5(h5_path: str, output_dir: str):
+    """
+    Reads the consolidated time-stepped H5 file.
+    - Flattens the training data into individual time-step samples.
+    - Groups the testing data by full trajectory.
+    """
+    print("="*60)
+    print("STARTING H5 TO .PT CONVERSION FOR TIME-STEPPED DATA")
+    print("="*60)
+
+    # --- 1. SETUP PATHS AND LOAD METADATA ---
+    os.makedirs(output_dir, exist_ok=True)
+    meta_json_path = h5_path.replace('.h5', '.json')
+
+    if not os.path.exists(h5_path):
+        raise FileNotFoundError(f"H5 file not found at: {h5_path}")
+    if not os.path.exists(meta_json_path):
+        raise FileNotFoundError(f"Metadata JSON file not found at: {meta_json_path}")
+
+    with open(meta_json_path, 'r') as f:
+        metadata = json.load(f)
+    
+    file_info = metadata.get("file_info", {})
+    group_names = sorted(list(file_info.keys()))
+    
+    if not group_names:
+        print("No groups found in the H5 file. Exiting.")
+        return
+        
+    print(f"Found {len(group_names)} total simulation trajectories in the metadata.")
+
+    # --- 2. SPLIT DATASET ---
+    random.seed(42) # Use a fixed seed for reproducibility
+    random.shuffle(group_names)
+
+    train_end_idx = round(len(group_names) * 0.8)
+    splits = {
+        'train': group_names[:train_end_idx],
+        'test': group_names[train_end_idx:]
+    }
+
+    print("\nSplitting data into sets based on trajectories:")
+    for split_name, groups in splits.items():
+        print(f"  - {split_name.upper()} set: {len(groups)} trajectories")
+
+    # --- 3. PROCESS EACH SPLIT AND SAVE TO A .PT FILE ---
+    with h5py.File(h5_path, 'r') as h5_file:
+        for split_name, groups_in_split in splits.items():
+            if not groups_in_split:
+                print(f"\n--- No trajectories for '{split_name}' split. Skipping. ---")
+                continue
+                
+            print(f"\n--- Processing '{split_name}' split ---")
+            
+            final_data_list: List[Dict[str, Any]] = []
+
+            # <<< MODIFIED: Conditional logic based on the split type >>>
+            if split_name == 'train':
+                # --- FLATTENING LOGIC FOR THE TRAINING SET ---
+                print("  -> Applying FLATTENING for the training set.")
+                for group_name in groups_in_split:
+                    if group_name not in h5_file: continue
+                    group = h5_file[group_name]
+                    meta_info = file_info.get(group_name, {})
+                    num_steps = meta_info.get('num_steps', 0)
+
+                    # Load static data once per trajectory
+                    coordinates = torch.tensor(group['coordinates'][:], dtype=torch.float32)
+                    node_type = torch.tensor(group['node_type'][:], dtype=torch.float32)
+                    
+                    # Load all time-varying data for the trajectory
+                    current_velocities_all_steps = torch.tensor(group['current_velocities'][:], dtype=torch.float32)
+                    target_velocities_all_steps = torch.tensor(group['target_velocities'][:], dtype=torch.float32)
+
+                    # Loop through each time-step and append it as an individual sample
+                    for step_idx in range(num_steps):
+                        sample_data = {
+                            'coordinates': coordinates, 'node_type': node_type,
+                            'current_velocities': current_velocities_all_steps[step_idx],
+                            'target_velocities': target_velocities_all_steps[step_idx],
+                            'meta_info': meta_info
+                        }
+                        final_data_list.append(sample_data)
+            
+            else: # For 'test' split
+                # --- GROUPING LOGIC FOR THE TESTING SET ---
+                print("  -> Applying GROUPING for the testing set.")
+                for group_name in groups_in_split:
+                    if group_name not in h5_file: continue
+                    group = h5_file[group_name]
+                    meta_info = file_info.get(group_name, {})
+
+                    # Load all data for the entire trajectory at once
+                    trajectory_sample = {
+                        'coordinates': torch.tensor(group['coordinates'][:], dtype=torch.float32),
+                        'node_type': torch.tensor(group['node_type'][:], dtype=torch.float32),
+                        'current_velocities': torch.tensor(group['current_velocities'][:], dtype=torch.float32),
+                        'target_velocities': torch.tensor(group['target_velocities'][:], dtype=torch.float32),
+                        'meta_info': meta_info
+                    }
+                    final_data_list.append(trajectory_sample)
+
+            # --- Saving Logic (same for both splits) ---
+            base_filename = os.path.splitext(os.path.basename(h5_path))[0]
+            output_pt_path = os.path.join(output_dir, f"{base_filename}_{split_name}.pt")
+            
+            torch.save(final_data_list, output_pt_path)
+
+            if split_name == 'train':
+                print(f"  -> Successfully saved {len(final_data_list)} FLATTENED time-step samples to: {output_pt_path}")
+            else:
+                print(f"  -> Successfully saved {len(final_data_list)} GROUPED trajectories to: {output_pt_path}")
+
+    print("\n" + "="*60)
+    print("CONVERSION COMPLETE")
+    print(f"Output .pt files are located in: {output_dir}")
+    print("="*60)
+
+def main():
+    H5_FILE_PATH = r"D:\ANK_official\a_Dataset_generated\preprocessed_for_model\timestep_data\final_data_timestep_data.h5"
+    PT_OUTPUT_DIR = r"D:\ANK_official\a_Dataset_generated\preprocessed_for_model\timestep_data\pt_files"
+    create_pt_files_from_h5(H5_FILE_PATH, PT_OUTPUT_DIR)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/cleanroom/output/best_model.pth b/cleanroom/output/best_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..18dca07d373a50eb463014d73be0342368bf2d8d
--- /dev/null
+++ b/cleanroom/output/best_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93e67b8b58b57fc50636264b46db19020ceb89747422dd84ba07653409d1748b
+size 3078463
diff --git a/cleanroom/output/checkpoint_epoch_40.pth b/cleanroom/output/checkpoint_epoch_40.pth
new file mode 100644
index 0000000000000000000000000000000000000000..607db1a90a62ea7fb1eb24f6cfcbc1a13e946ca2
--- /dev/null
+++ b/cleanroom/output/checkpoint_epoch_40.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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\ No newline at end of file
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diff --git a/cleanroom/rawdataset/final_data_timestep_data_test.pt b/cleanroom/rawdataset/final_data_timestep_data_test.pt
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--- /dev/null
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diff --git a/cleanroom/readingptfile.ipynb b/cleanroom/readingptfile.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4f9acb87efdaba2bc36d23b19707eaa675604f31
--- /dev/null
+++ b/cleanroom/readingptfile.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "899c057f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2522c5ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load = np.load(\"/home/gd_user1/AnK/project_PINN/cleanroom/outputs_800/rollout_results_epoch_800.npy\", allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5f52b647",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['ground_truth_trajectory', 'predicted_trajectory', 'coordinates', 'node_type', 'meta_info'])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "load[2].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7de70392",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[ 0.1196, -0.4992, -0.0659],\n",
+       "         [ 0.1868,  0.2426,  0.0847],\n",
+       "         [-0.0145, -0.5594, -0.0683],\n",
+       "         ...,\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000]],\n",
+       "\n",
+       "        [[ 0.1195, -0.4993, -0.0659],\n",
+       "         [ 0.1867,  0.2427,  0.0849],\n",
+       "         [-0.0145, -0.5594, -0.0683],\n",
+       "         ...,\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000]],\n",
+       "\n",
+       "        [[ 0.1195, -0.4993, -0.0659],\n",
+       "         [ 0.1867,  0.2427,  0.0851],\n",
+       "         [-0.0146, -0.5595, -0.0683],\n",
+       "         ...,\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.1211, -0.5029, -0.0667],\n",
+       "         [ 0.1806,  0.2450,  0.0863],\n",
+       "         [-0.0136, -0.5589, -0.0679],\n",
+       "         ...,\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000]],\n",
+       "\n",
+       "        [[ 0.1211, -0.5029, -0.0667],\n",
+       "         [ 0.1807,  0.2450,  0.0860],\n",
+       "         [-0.0134, -0.5588, -0.0680],\n",
+       "         ...,\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000]],\n",
+       "\n",
+       "        [[ 0.1211, -0.5030, -0.0667],\n",
+       "         [ 0.1808,  0.2450,  0.0857],\n",
+       "         [-0.0133, -0.5587, -0.0681],\n",
+       "         ...,\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000],\n",
+       "         [ 0.0000,  0.0000,  0.0000]]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "load[2]['ground_truth_trajectory'][1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "da254464",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([48, 22626, 3])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "load[2]['predicted_trajectory'][:-1].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a0a625",
+   "metadata": {},
+   "source": [
+    "# Animation portion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6ffb64ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import r2_score\n",
+    "import torch\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e76d49ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_data= np.load(r'/home/gd_user1/AnK/project_PINN/cleanroom/outputs_800/rollout_results_epoch_800.npy', allow_pickle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f91018c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(val_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "81377251",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_element=val_data[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "012e8c43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 7.3562e+00,  1.5680e+00,  3.2644e+00],\n",
+       "        [ 5.9370e+00,  1.1662e+00,  3.9563e+00],\n",
+       "        [ 6.5281e+00,  1.8776e+00,  2.4812e+00],\n",
+       "        ...,\n",
+       "        [-3.0000e-03,  2.5984e+00,  2.2542e+00],\n",
+       "        [-3.0000e-03,  2.7853e+00,  2.1156e+00],\n",
+       "        [-3.0000e-03,  2.7787e+00,  2.3665e+00]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " ijk = first_element['coordinates']\n",
+    " ijk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "743effdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating animation with error plot...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing frame for time step 43\n",
+      "GIF saved to: /home/gd_user1/AnK/project_PINN/cleanroom/animation/animation_testing.gif\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "gif_path_3 = \"/home/gd_user1/AnK/project_PINN/cleanroom/animation/animation_testing.gif\"\n",
+    "geom = pv.Arrow()\n",
+    "sargs_true = {'title': 'Ground Truth Velocity (m/s)'}\n",
+    "sargs_pred = {'title': 'Predicted Velocity (m/s)'}\n",
+    "# New scalar bar arguments for the error plot\n",
+    "sargs_error = {'title': 'Error Magnitude (m/s)'}\n",
+    "\n",
+    "# MODIFICATION: Changed shape to (1, 3) and increased window size\n",
+    "pl = pv.Plotter(shape=(1, 3), border=False, off_screen=True, window_size=[2400, 800])\n",
+    "pl.set_background(color=[242/255, 242/255, 242/255])\n",
+    "\n",
+    "# Camera setup\n",
+    "pl.link_views()\n",
+    "pl.show_axes()\n",
+    "pl.view_isometric()\n",
+    "\n",
+    "# Open GIF writer\n",
+    "pl.open_gif(gif_path_3)\n",
+    "print(\"Generating animation with error plot...\")\n",
+    "\n",
+    "for i in range(1, 46, 2):\n",
+    "    # first_element = val_data[i]\n",
+    "    gt_vel = first_element['ground_truth_trajectory'][i].cpu().numpy()\n",
+    "    pred_velo = first_element['predicted_trajectory'][i-1].cpu().numpy()\n",
+    "    ijk = first_element['coordinates'].cpu().numpy()\n",
+    "    mesh = pv.PolyData(ijk)\n",
+    "    \n",
+    "    # --- Update mesh data for all vectors ---\n",
+    "    mesh['vectors1'] = gt_vel\n",
+    "    mesh['vectors2'] = pred_velo\n",
+    "    \n",
+    "    magnitudes = np.linalg.norm(gt_vel, axis=1)\n",
+    "    mesh['scalars1'] = magnitudes\n",
+    "    \n",
+    "    magnitudes2 = np.linalg.norm(pred_velo, axis=1)\n",
+    "    mesh['scalars2'] = magnitudes2\n",
+    "    \n",
+    "    # --- NEW: Calculate and add error data to the mesh ---\n",
+    "    error_vectors = gt_vel - pred_velo\n",
+    "    error_magnitudes = np.linalg.norm(error_vectors, axis=1)\n",
+    "    mesh['error_vectors'] = error_vectors\n",
+    "    mesh['error_magnitudes'] = error_magnitudes\n",
+    "    \n",
+    "    # --- Create all three sets of glyphs ---\n",
+    "    glyphs = mesh.glyph(orient='vectors1', scale='scalars1', factor=2, geom=geom)\n",
+    "    glyphs2 = mesh.glyph(orient='vectors2', scale='scalars2', factor=0.05, geom=geom)\n",
+    "    # NEW: Create glyphs for the error vectors. Using a larger factor to make them visible.\n",
+    "    error_glyphs = mesh.glyph(orient='error_vectors', scale='error_magnitudes', factor=10, geom=geom)\n",
+    "    \n",
+    "    # Clear previous meshes from all subplots\n",
+    "    pl.clear()\n",
+    "    \n",
+    "    # --- Add meshes to the three subplots ---\n",
+    "    # Subplot 1: Ground Truth\n",
+    "    pl.subplot(0, 0)\n",
+    "    pl.add_mesh(glyphs, show_scalar_bar=True, lighting=False, cmap='viridis', scalar_bar_args=sargs_true)\n",
+    "    pl.add_text(f\"Ground Truth - Time: {i}\", position='upper_edge', font_size=10)\n",
+    "    \n",
+    "    # Subplot 2: Prediction\n",
+    "    pl.subplot(0, 1)\n",
+    "    pl.add_mesh(glyphs2, show_scalar_bar=True, lighting=False, cmap='viridis', scalar_bar_args=sargs_pred)\n",
+    "    pl.add_text(f\"Prediction - Time: {i}\", position='upper_edge', font_size=10)\n",
+    "    \n",
+    "    # NEW: Subplot 3: Prediction Error\n",
+    "    pl.subplot(0, 2)\n",
+    "    pl.add_mesh(error_glyphs, scalars='error_magnitudes', show_scalar_bar=True, lighting=False, cmap='Reds', scalar_bar_args=sargs_error)\n",
+    "    pl.add_text(f\"Prediction Error - Time: {i}\", position='upper_edge', font_size=10)\n",
+    "    \n",
+    "    # Write the combined frame to the GIF\n",
+    "    pl.write_frame()\n",
+    "    \n",
+    "    print(f\"Processing frame for time step {i}\", end='\\r')\n",
+    "\n",
+    "# Close the GIF writer\n",
+    "pl.close()\n",
+    "print(f\"\\nGIF saved to: {gif_path_3}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "graph_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cleanroom/surrogateAI/__pycache__/clean_eval.cpython-310.pyc b/cleanroom/surrogateAI/__pycache__/clean_eval.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4fd6113e682763673a3dd5560533583d4ad7165f
Binary files /dev/null and b/cleanroom/surrogateAI/__pycache__/clean_eval.cpython-310.pyc differ
diff --git a/cleanroom/surrogateAI/clean_eval.py b/cleanroom/surrogateAI/clean_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..6322070c16bf19ccf8114a8e4bd012bdff61878d
--- /dev/null
+++ b/cleanroom/surrogateAI/clean_eval.py
@@ -0,0 +1,105 @@
+import torch
+import torch.nn as nn
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def _rollout(model, initial_state, num_steps, norm_stats):
+    """
+    Performs a multi-step rollout simulation using the trained model.
+    The model's output from one step is used as the input for the next.
+    Applies normalization to inputs and de-normalization to outputs.
+    """
+    # --- UNPACK NORMALIZATION STATS ---
+    coords_mean = norm_stats['coords_mean'].to(device)
+    coords_std = norm_stats['coords_std'].to(device)
+    vel_mean = norm_stats['vel_mean'].to(device)
+    vel_std = norm_stats['vel_std'].to(device)
+    inlet_vel_mean = norm_stats['inlet_vel_mean'].to(device)
+    inlet_vel_std = norm_stats['inlet_vel_std'].to(device)
+    epsilon = 1e-7
+
+    predicted_trajectory = []
+
+    current_velocities = initial_state['current_velocities'].float().to(device)
+    coordinates = initial_state['coordinates'].float().to(device)
+    node_type = initial_state['node_type'].float().to(device)
+
+    for step in range(num_steps):
+        raw_vel = initial_state['meta_info']['velocity']
+        if torch.is_tensor(raw_vel):
+            inlet_vel = raw_vel.clone().detach().float().to(device)
+        else:
+            inlet_vel = torch.tensor(raw_vel, dtype=torch.float32).to(device)
+        
+        if inlet_vel.dim() > 1:
+            inlet_vel = inlet_vel.flatten()[0]
+        elif inlet_vel.dim() == 1 and inlet_vel.numel() == 1:
+            inlet_vel = inlet_vel.item()
+
+        if isinstance(inlet_vel, torch.Tensor):
+            norm_inlet_scalar = (inlet_vel - inlet_vel_mean) / (inlet_vel_std + epsilon)
+        else:
+            norm_inlet_scalar = (inlet_vel - inlet_vel_mean.item()) / (inlet_vel_std.item() + epsilon)
+        
+        num_nodes = coordinates.shape[0]
+        
+        if isinstance(norm_inlet_scalar, torch.Tensor):
+            norm_inlet_vel = norm_inlet_scalar.expand(num_nodes).float()
+        else:
+            norm_inlet_vel = torch.full((num_nodes,), float(norm_inlet_scalar), dtype=torch.float32, device=device)
+
+        norm_coords = (coordinates - coords_mean) / (coords_std + epsilon)
+        norm_current_vel = (current_velocities - vel_mean) / (vel_std + epsilon)
+
+        norm_coords_batch = norm_coords.unsqueeze(0)
+        node_type_batch = node_type.unsqueeze(0)
+        norm_current_vel_batch = norm_current_vel.unsqueeze(0)
+        norm_inlet_vel_feature_batch = norm_inlet_vel.unsqueeze(0).unsqueeze(-1)
+    
+        final_input = torch.cat([
+            norm_coords_batch,
+            node_type_batch,
+            norm_current_vel_batch,
+            norm_inlet_vel_feature_batch
+        ], dim=-1)
+
+        with torch.no_grad():
+            predicted_normalized_velocities = model(final_input).squeeze(0)
+
+        predicted_next_velocities = (predicted_normalized_velocities * (vel_std + epsilon)) + vel_mean
+        
+        predicted_trajectory.append(predicted_next_velocities)
+        
+        current_velocities = predicted_next_velocities
+
+    return torch.stack(predicted_trajectory, dim=0)
+
+def evaluate(model, trajectory_data, norm_stats):
+    """
+    Takes a full validation trajectory, normalizes it, and generates a model rollout prediction.
+    
+    Args:
+        model: The trained Transolver model.
+        trajectory_data: A dictionary containing the full, grouped validation trajectory.
+        norm_stats: A dictionary with the mean and std of the training data.
+    """
+    num_steps = trajectory_data['current_velocities'].shape[0]
+    
+    initial_state = {
+        'coordinates': trajectory_data['coordinates'].float(),
+        'node_type': trajectory_data['node_type'].float(),
+        'current_velocities': trajectory_data['current_velocities'][0].float(), # First step
+        'meta_info': trajectory_data['meta_info']
+    }
+    
+    predicted_trajectory = _rollout(model, initial_state, num_steps, norm_stats)
+
+    results = {
+        'ground_truth_trajectory': trajectory_data['target_velocities'].float(),
+        'predicted_trajectory': predicted_trajectory,
+        'coordinates': trajectory_data['coordinates'].float(),
+        'node_type': trajectory_data['node_type'].float(),
+        'meta_info': trajectory_data['meta_info']
+    }
+    
+    return None, results
\ No newline at end of file
diff --git a/cleanroom/surrogateAI/opening.py b/cleanroom/surrogateAI/opening.py
new file mode 100644
index 0000000000000000000000000000000000000000..b54c7093e49f774579696a8281ed5c612afc145e
--- /dev/null
+++ b/cleanroom/surrogateAI/opening.py
@@ -0,0 +1,6 @@
+import numpy as np
+file_path = '/home/gd_user1/AnK/project_PINN/cleanroom/second_output/rollout_results_epoch_130.npy'
+data = np.load(file_path,allow_pickle=True)
+print(data[0]['meta_info']['velocity'])
+print(data[0]['ground_truth_trajectory'][1])
+print(data[0]['predicted_trajectory'][1])
\ No newline at end of file
diff --git a/cleanroom/surrogateAI/second_train.py b/cleanroom/surrogateAI/second_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5160b7ac81156a99ba02764d534a22df19b4085
--- /dev/null
+++ b/cleanroom/surrogateAI/second_train.py
@@ -0,0 +1,551 @@
+import os
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+from timm.models.layers import trunc_normal_
+from einops import rearrange
+import numpy as np
+from typing import List, Dict, Any
+import matplotlib.pyplot as plt
+import json
+import random
+import time
+from clean_eval import evaluate as rollout_evaluate
+
+# ============================================================================ 
+# 1. PYTORCH DATASET CLASS FOR TIME-STEPPED DATA
+# ============================================================================ 
+class TimeStepDataset(Dataset):
+    """Dataset for flattened timestep training data"""
+
+    def __init__(self, pt_file_path: str):
+        if not os.path.exists(pt_file_path):
+            raise FileNotFoundError(f"Dataset file not found at: {pt_file_path}")
+        self.data = torch.load(pt_file_path, weights_only=False)
+        print(f"Loaded {len(self.data)} timesteps from {pt_file_path}")
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, idx: int) -> Dict[str, Any]:
+        sample = self.data[idx]
+        return {
+            'coordinates': sample['coordinates'],
+            'node_type': sample['node_type'],
+            'current_velocities': sample['current_velocities'],
+            'target_velocities': sample['target_velocities'],
+            'meta_info': sample['meta_info']
+        }
+
+class TrajectoryDataset(Dataset):
+    """Dataset for full trajectory evaluation data"""
+
+    def __init__(self, pt_file_path: str):
+        if not os.path.exists(pt_file_path):
+            raise FileNotFoundError(f"Dataset file not found at: {pt_file_path}")
+        self.trajectories = torch.load(pt_file_path, weights_only=False)
+        print(f"Loaded {len(self.trajectories)} trajectories from {pt_file_path}")
+
+    def __len__(self) -> int:
+        return len(self.trajectories)
+
+    def __getitem__(self, idx: int) -> Dict[str, Any]:
+        return self.trajectories[idx]
+
+# ============================================================================ 
+# 2. TRANSOLVER MODEL ARCHITECTURE (Adapted for Time-Dependent Prediction)
+# ============================================================================ 
+ACTIVATION = {'gelu': nn.GELU, 'tanh': nn.Tanh, 'sigmoid': nn.Sigmoid, 'relu': nn.ReLU, 'leaky_relu': nn.LeakyReLU(0.1)}
+
+class MLP(nn.Module):
+    def __init__(self, n_input, n_hidden, n_output, n_layers=1, act='gelu', res=True):
+        super(MLP, self).__init__()
+        if act in ACTIVATION.keys():
+            act = ACTIVATION[act]
+        else:
+            raise NotImplementedError
+        self.linear_pre = nn.Sequential(nn.Linear(n_input, n_hidden), act())
+        self.linear_post = nn.Linear(n_hidden, n_output)
+        self.linears = nn.ModuleList([nn.Sequential(nn.Linear(n_hidden, n_hidden), act()) for _ in range(n_layers)])
+        self.res = res
+
+    def forward(self, x):
+        x = self.linear_pre(x)
+        for i in range(len(self.linears)):
+            if self.res:
+                x = self.linears[i](x) + x
+            else:
+                x = self.linears[i](x)
+        x = self.linear_post(x)
+        return x
+
+class Physics_Attention_Irregular_Mesh(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dropout=0., slice_num=64):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.dim_head = dim_head
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.softmax = nn.Softmax(dim=-1)
+        self.dropout = nn.Dropout(dropout)
+        self.temperature = nn.Parameter(torch.ones([1, heads, 1, 1]) * 0.5)
+        self.in_project_x = nn.Linear(dim, inner_dim)
+        self.in_project_fx = nn.Linear(dim, inner_dim)
+        self.in_project_slice = nn.Linear(dim_head, slice_num)
+        torch.nn.init.orthogonal_(self.in_project_slice.weight)
+        self.to_q = nn.Linear(dim_head, dim_head, bias=False)
+        self.to_k = nn.Linear(dim_head, dim_head, bias=False)
+        self.to_v = nn.Linear(dim_head, dim_head, bias=False)
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))
+
+    def forward(self, x):
+        B, N, C = x.shape
+        fx_mid = self.in_project_fx(x).reshape(B, N, self.heads, self.dim_head).permute(0, 2, 1, 3).contiguous()
+        x_mid = self.in_project_x(x).reshape(B, N, self.heads, self.dim_head).permute(0, 2, 1, 3).contiguous()
+        slice_weights = self.softmax(self.in_project_slice(x_mid) / self.temperature)
+        slice_norm = slice_weights.sum(2)
+        slice_token = torch.einsum("bhnc,bhng->bhgc", fx_mid, slice_weights)
+        slice_token = slice_token / ((slice_norm + 1e-5)[:, :, :, None].repeat(1, 1, 1, self.dim_head))
+        q_slice_token = self.to_q(slice_token)
+        k_slice_token = self.to_k(slice_token)
+        v_slice_token = self.to_v(slice_token)
+        dots = torch.matmul(q_slice_token, k_slice_token.transpose(-1, -2)) * self.scale
+        attn = self.softmax(dots)
+        attn = self.dropout(attn)
+        out_slice_token = torch.matmul(attn, v_slice_token)
+        out_x = torch.einsum("bhgc,bhng->bhnc", out_slice_token, slice_weights)
+        out_x = rearrange(out_x, 'b h n d -> b n (h d)')
+        out = self.to_out(out_x)
+        return out
+
+class Transolver_block(nn.Module):
+    def __init__(self, num_heads, hidden_dim, dropout, act='gelu', mlp_ratio=4, last_layer=False, out_dim=1, slice_num=32):
+        super().__init__()
+        self.last_layer = last_layer
+        self.ln_1 = nn.LayerNorm(hidden_dim)
+        self.Attn = Physics_Attention_Irregular_Mesh(hidden_dim, heads=num_heads, dim_head=hidden_dim // num_heads, dropout=dropout, slice_num=slice_num)
+        self.ln_2 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP(hidden_dim, hidden_dim * mlp_ratio, hidden_dim, n_layers=0, res=False, act=act)
+        if self.last_layer:
+            self.ln_3 = nn.LayerNorm(hidden_dim)
+            self.mlp2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, fx):
+        fx2 = self.Attn(self.ln_1(fx)) + fx
+        fx = self.mlp(self.ln_2(fx2)) + fx2
+        if self.last_layer:
+            return self.mlp2(self.ln_3(fx))
+        return fx
+
+class Model(nn.Module):
+    def __init__(self, in_dim=13, out_dim=3, n_layers=8, n_hidden=256, dropout=0, n_head=8, act='gelu', mlp_ratio=2, slice_num=32):
+        super(Model, self).__init__()
+        self.preprocess = MLP(in_dim, n_hidden * 2, n_hidden, n_layers=0, res=False, act=act)
+        self.n_hidden = n_hidden
+        self.blocks = nn.ModuleList([Transolver_block(num_heads=n_head, hidden_dim=n_hidden, dropout=dropout,
+                                                      act=act, mlp_ratio=mlp_ratio,
+                                                      out_dim=out_dim,
+                                                      slice_num=slice_num,
+                                                      last_layer=(_ == n_layers - 1))
+                                     for _ in range(n_layers)])
+        self.initialize_weights()
+        self.placeholder = nn.Parameter((1 / n_hidden) * torch.rand(n_hidden, dtype=torch.float))
+
+    def initialize_weights(self):
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, (nn.LayerNorm, nn.BatchNorm1d)):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x):
+        fx = self.preprocess(x)
+        fx = fx + self.placeholder[None, None, :] 
+        for block in self.blocks:
+            fx = block(fx)
+        return fx
+
+# ============================================================================ 
+# 3. CHECKPOINTING FUNCTIONS
+# ============================================================================ 
+def save_checkpoint(model, optimizer, scheduler, epoch, loss, path, norm_stats):
+    """Save training checkpoint"""
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'scheduler_state_dict': scheduler.state_dict() if scheduler else None,
+        'loss': loss,
+        'norm_stats': norm_stats  # Save normalization stats
+    }
+    torch.save(checkpoint, path)
+    print(f"Checkpoint saved: {path}")
+
+def load_checkpoint(model, optimizer, scheduler, path):
+    """Load training checkpoint"""
+    checkpoint = torch.load(path, map_location='cpu')
+    model.load_state_dict(checkpoint['model_state_dict'])
+    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+    if scheduler and checkpoint.get('scheduler_state_dict'):
+        scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+    
+    # Load norm_stats if they exist, otherwise return None
+    norm_stats = checkpoint.get('norm_stats', None)
+    if norm_stats is None:
+        print("Warning: Normalization stats not found in checkpoint.")
+        
+    return checkpoint['epoch'], checkpoint['loss'], norm_stats
+
+# ============================================================================ 
+# 4. EVALUATION FUNCTIONS
+# ============================================================================ 
+def create_wall_mask(node_type):
+    """Create mask to ignore wall nodes during loss calculation"""
+    wall_class_index = 5  # Assuming walls are class 5
+    node_class_indices = torch.argmax(node_type, dim=-1)
+    mask = (node_class_indices != wall_class_index).float().unsqueeze(-1)
+    return mask
+
+def evaluate_rollout(model, trajectory_loader, device, norm_stats):
+    """Run rollout evaluation on full trajectories"""
+    model.eval()
+    results = []
+    
+    print(f"Starting rollout evaluation on {len(trajectory_loader)} trajectories...")
+    
+    with torch.no_grad():
+        for i, trajectory in enumerate(trajectory_loader):
+            try:
+                print(f"\nProcessing trajectory {i+1}/{len(trajectory_loader)}")
+                
+                # Since trajectory_loader has batch_size=1, we need to extract the single trajectory
+                single_trajectory = {}
+                for key, value in trajectory.items():
+                    if key != 'meta_info':
+                        # Remove the batch dimension
+                        single_trajectory[key] = value.squeeze(0)
+                    else:
+                        # meta_info is special - it's a dict where each value is a list/tensor due to batching
+                        # We need to extract the first element from each value in the meta_info dict
+                        single_trajectory[key] = {}
+                        for meta_key, meta_value in value.items():
+                            if isinstance(meta_value, (list, tuple)):
+                                single_trajectory[key][meta_key] = meta_value[0]
+                            elif isinstance(meta_value, dict):
+                                # Handle nested dictionaries (like node_type_counts)
+                                single_trajectory[key][meta_key] = {}
+                                for nested_key, nested_value in meta_value.items():
+                                    if isinstance(nested_value, (list, tuple)):
+                                        single_trajectory[key][meta_key][nested_key] = nested_value[0]
+                                    elif torch.is_tensor(nested_value) and nested_value.numel() > 1:
+                                        single_trajectory[key][meta_key][nested_key] = nested_value[0]
+                                    else:
+                                        single_trajectory[key][meta_key][nested_key] = nested_value
+                            elif torch.is_tensor(meta_value) and meta_value.numel() > 1:
+                                # For tensors with more than one element, take the first element
+                                single_trajectory[key][meta_key] = meta_value[0]
+                            else:
+                                # For scalars or other types, keep as is
+                                single_trajectory[key][meta_key] = meta_value
+                
+                # Pass the normalization statistics to the evaluation function
+                _, traj_result = rollout_evaluate(model, single_trajectory, norm_stats)
+                results.append(traj_result)
+                print(f"Successfully processed trajectory {i+1}")
+                
+            except Exception as e:
+                print(f"Error processing trajectory {i+1}: {str(e)}")
+                print(f"Trajectory data shapes:")
+                for key, value in trajectory.items():
+                    if hasattr(value, 'shape'):
+                        print(f"  {key}: {value.shape}")
+                    else:
+                        print(f"  {key}: {type(value)} - {value}")
+                raise e
+                
+    print(f"Completed rollout evaluation on {len(results)} trajectories")
+    return results
+# ============================================================================ 
+# 5. MAIN TRAINING SCRIPT
+# ============================================================================ 
+def main():
+    script_start_time = time.time()
+    # --- CONFIGURATION ---
+    preprocessed_dir = r"/home/gd_user1/AnK/project_PINN/cleanroom/rawdataset"
+    base_filename = "final_data_timestep_data"
+    output_dir = r"/home/gd_user1/AnK/project_PINN/cleanroom/second_output/output_2"
+
+    train_pt_path = os.path.join(preprocessed_dir, f"{base_filename}_train.pt")
+    val_pt_path = os.path.join(preprocessed_dir, f"{base_filename}_test.pt")
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    hparams = {
+        'lr': 0.0005, 'batch_size': 1, 'nb_epochs': 400,
+        'in_dim': 13,  # coordinates(3) + node_type(6) + current_vel(3) + inlet_vel(1)
+        'out_dim': 3, 'n_hidden': 256, 'n_layers': 6, 'n_head': 8, 'slice_num': 32,
+        'checkpoint_freq': 10, 'eval_freq': 10
+    }
+
+    # --- SETUP ---
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    if torch.cuda.is_available():
+        print(f"GPU: {torch.cuda.get_device_name(0)}")
+        print(f"CUDA version: {torch.version.cuda}")
+
+    # Set random seeds for reproducibility
+    torch.manual_seed(42)
+    random.seed(42)
+    np.random.seed(42)
+
+    # --- DATALOADERS ---
+    print("Loading datasets...")
+    train_dataset = TimeStepDataset(train_pt_path)
+    val_timestep_dataset = TimeStepDataset(val_pt_path)  # For single-step validation
+    test_trajectory_dataset = TrajectoryDataset(val_pt_path)  # For rollout evaluation
+
+    train_loader = DataLoader(train_dataset, batch_size=hparams['batch_size'], shuffle=True)
+    val_timestep_loader = DataLoader(val_timestep_dataset, batch_size=hparams['batch_size'], shuffle=False)
+    test_trajectory_loader = DataLoader(test_trajectory_dataset, batch_size=1, shuffle=False)
+
+    # --- COMPUTE NORMALIZATION STATISTICS FROM TRAINING DATA ---
+    def compute_normalization_stats(train_dataset):
+        """Compute normalization statistics from training dataset"""
+        coords_list = []
+        vel_list = []
+        inlet_vel_list = []
+
+        print("Computing normalization statistics from training data...")
+
+        for sample in train_dataset:
+            coords_list.append(sample['coordinates'])
+            vel_list.append(sample['current_velocities'])
+            # Handle inlet velocity (it's in meta_info and may be tensor or scalar)
+            inlet_vel = sample['meta_info']['velocity']
+            if torch.is_tensor(inlet_vel):
+                inlet_vel_list.append(inlet_vel.item())
+            else:
+                inlet_vel_list.append(float(inlet_vel))
+
+        # Stack and compute statistics
+        coords_all = torch.stack(coords_list)  # [num_samples, num_nodes, 3]
+        vel_all = torch.stack(vel_list)        # [num_samples, num_nodes, 3]
+        inlet_vel_all = torch.tensor(inlet_vel_list)  # [num_samples]
+
+        stats = {
+            'coords_mean': coords_all.mean(dim=[0, 1]),  # Mean across samples and nodes
+            'coords_std': coords_all.std(dim=[0, 1]),
+            'vel_mean': vel_all.mean(dim=[0, 1]),
+            'vel_std': vel_all.std(dim=[0, 1]),
+            'inlet_vel_mean': inlet_vel_all.mean(),
+            'inlet_vel_std': inlet_vel_all.std()
+        }
+
+        print(f"Computed stats - Coords mean: {stats['coords_mean']}, std: {stats['coords_std']}")
+        print(f"Velocities mean: {stats['vel_mean']}, std: {stats['vel_std']}")
+        print(f"Inlet vel mean: {stats['inlet_vel_mean']:.6f}, std: {stats['inlet_vel_std']:.6f}")
+
+        return stats
+
+    # Compute normalization statistics
+    norm_stats = compute_normalization_stats(train_dataset)
+    coords_mean = norm_stats['coords_mean'].to(device)
+    coords_std = norm_stats['coords_std'].to(device)
+    vel_mean = norm_stats['vel_mean'].to(device)
+    vel_std = norm_stats['vel_std'].to(device)
+    inlet_vel_mean = norm_stats['inlet_vel_mean'].to(device)
+    inlet_vel_std = norm_stats['inlet_vel_std'].to(device)
+    epsilon = 1e-8  # Small constant to prevent division by zero
+
+    # --- MODEL, OPTIMIZER, SCHEDULER SETUP ---
+    model = Model(in_dim=hparams['in_dim'], out_dim=hparams['out_dim'], 
+                  n_hidden=hparams['n_hidden'], n_layers=hparams['n_layers'],
+                  n_head=hparams['n_head'], slice_num=hparams['slice_num']).to(device)
+    
+    optimizer = torch.optim.Adam(model.parameters(), lr=hparams['lr'])
+    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.9)
+    criterion = nn.MSELoss()
+
+    # Initialize training variables
+    start_epoch = 0
+    best_val_loss = float('inf')
+
+    # Check for existing checkpoint to resume training
+    latest_checkpoint_path = os.path.join(output_dir, 'checkpoint_latest.pth')
+    if os.path.exists(latest_checkpoint_path):
+        print(f"Found existing checkpoint: {latest_checkpoint_path}")
+        try:
+            start_epoch, last_loss, loaded_norm_stats = load_checkpoint(
+                model, optimizer, scheduler, latest_checkpoint_path
+            )
+            if loaded_norm_stats is not None:
+                norm_stats = loaded_norm_stats
+                # Update the normalization tensors
+                coords_mean = norm_stats['coords_mean'].to(device)
+                coords_std = norm_stats['coords_std'].to(device)
+                vel_mean = norm_stats['vel_mean'].to(device)
+                vel_std = norm_stats['vel_std'].to(device)
+                inlet_vel_mean = norm_stats['inlet_vel_mean'].to(device)
+                inlet_vel_std = norm_stats['inlet_vel_std'].to(device)
+                print("Loaded normalization statistics from checkpoint.")
+            print(f"Resuming training from epoch {start_epoch}")
+            best_val_loss = last_loss
+        except Exception as e:
+            print(f"Error loading checkpoint: {e}")
+            print("Starting fresh training...")
+            start_epoch = 0
+            best_val_loss = float('inf')
+
+    print(f"Starting training from epoch {start_epoch} to {hparams['nb_epochs']}")
+    print("="*60)
+
+    train_loss_history = []
+    val_loss_history = []
+    lr_history = []
+    val_epochs = []
+
+    # --- TRAINING LOOP ---
+    for epoch in range(start_epoch, hparams['nb_epochs']):
+        epoch_start_time = time.time()
+        model.train()
+        total_train_loss = 0.0
+
+        for batch_idx, batch in enumerate(train_loader):
+            coordinates = batch['coordinates'].to(device)
+            node_type = batch['node_type'].to(device)
+            current_velocities = batch['current_velocities'].to(device)
+            target_velocities = batch['target_velocities'].to(device)
+
+            inlet_velocities = batch['meta_info']['velocity']
+            if torch.is_tensor(inlet_velocities):
+                inlet_vel_tensor = inlet_velocities.clone().detach().float().to(device)
+            else:
+                inlet_vel_tensor = torch.tensor(inlet_velocities, dtype=torch.float32).to(device)
+            
+            # --- NORMALIZE FEATURES ---
+            norm_coords = (coordinates - coords_mean) / (coords_std + epsilon)
+            norm_current_vel = (current_velocities - vel_mean) / (vel_std + epsilon)
+            norm_target_vel = (target_velocities - vel_mean) / (vel_std + epsilon)
+            norm_inlet_vel = (inlet_vel_tensor - inlet_vel_mean) / (inlet_vel_std + epsilon)
+
+            batch_size, num_nodes, _ = coordinates.shape
+            inlet_vel_feature = norm_inlet_vel.view(batch_size, 1, 1).expand(batch_size, num_nodes, 1)
+
+            # Input: [normalized coordinates, node_type, normalized velocities, normalized inlet_velocity]
+            input_features = torch.cat([norm_coords, node_type, norm_current_vel, inlet_vel_feature], dim=-1)
+
+            optimizer.zero_grad()
+            predicted_normalized_velocities = model(input_features)
+
+            # Apply wall masking
+            mask = create_wall_mask(node_type)
+            loss = criterion(predicted_normalized_velocities * mask, norm_target_vel * mask)
+            loss.backward()
+            optimizer.step()
+
+            total_train_loss += loss.item()
+
+            # Progress update every 50 batches
+            if batch_idx % 50 == 0:
+                print(f"Epoch {epoch+1}/{hparams['nb_epochs']} | Batch {batch_idx+1}/{len(train_loader)} | Loss: {loss.item():.6f}")
+
+        scheduler.step()
+        lr_history.append(optimizer.param_groups[0]['lr'])
+        epoch_time = time.time() - epoch_start_time
+        avg_train_loss = total_train_loss / len(train_loader)
+        train_loss_history.append(avg_train_loss)
+
+        print(f"Epoch {epoch+1}/{hparams['nb_epochs']} completed | Avg Train Loss: {avg_train_loss:.6f} | Time: {epoch_time:.2f}s")
+
+        # --- PERIODIC EVALUATION ---
+        if (epoch + 1) % hparams['eval_freq'] == 0 or epoch == hparams['nb_epochs'] - 1:
+            print(f"Epoch {epoch+1}/{hparams['nb_epochs']} | Train Loss: {avg_train_loss:.6f}")
+
+            # Rollout evaluation
+            print("  Running rollout evaluation...")
+            rollout_results = evaluate_rollout(model, test_trajectory_loader, device, norm_stats)
+
+            # Calculate loss from rollout
+            total_rollout_mse = 0.0
+            for result in rollout_results:
+                # Move tensors to the correct device
+                pred_traj = result['predicted_trajectory'].to(device)
+                gt_traj = result['ground_truth_trajectory'].to(device)
+                node_type = result['node_type'].to(device)
+
+                # Create a mask to ignore wall nodes
+                mask = create_wall_mask(node_type).unsqueeze(0)  # Add time dim for broadcasting
+
+                # Calculate masked MSE
+                loss = criterion(pred_traj * mask, gt_traj * mask)
+                total_rollout_mse += loss.item()
+            
+            avg_rollout_mse = total_rollout_mse / len(rollout_results)
+            
+            val_loss = avg_rollout_mse
+            val_loss_history.append(val_loss)
+            val_epochs.append(epoch + 1)
+            print(f"  Validation Rollout MSE: {val_loss:.6f}")
+
+            # Save rollout results
+            rollout_path = os.path.join(output_dir, f"rollout_results_epoch_{epoch+1}.npy")
+            np.save(rollout_path, rollout_results, allow_pickle=True) # allow_pickle is good practice for np.save with dicts
+            print(f"  -> Saved rollout results to {rollout_path}")
+
+            # Save best model based on rollout loss
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                best_model_path = os.path.join(output_dir, 'best_model.pth')
+                torch.save(model.state_dict(), best_model_path)
+                print(f"  -> New best model saved with validation loss: {best_val_loss:.6f}")
+
+        # --- PERIODIC CHECKPOINTING ---
+        if (epoch + 1) % hparams['checkpoint_freq'] == 0:
+            # Use validation loss if available, otherwise use training loss
+            checkpoint_loss = val_loss if 'val_loss' in locals() else avg_train_loss
+            checkpoint_path = os.path.join(output_dir, f'checkpoint_epoch_{epoch+1}.pth')
+            save_checkpoint(model, optimizer, scheduler, epoch + 1, checkpoint_loss, checkpoint_path, norm_stats)
+
+    # --- SAVE LATEST CHECKPOINT ---
+    save_checkpoint(model, optimizer, scheduler, hparams['nb_epochs'], best_val_loss,
+                   os.path.join(output_dir, 'checkpoint_latest.pth'), norm_stats)
+
+    print("="*60)
+    print("Training complete.")
+    print(f"Best validation loss: {best_val_loss:.6f}")
+
+    # --- PLOTTING AND SAVING LOSS HISTORY ---
+    plt.figure(figsize=(10, 5))
+    plt.plot(train_loss_history, label='Training Loss')
+    plt.plot(val_epochs, val_loss_history, 'o-', label='Validation Loss')
+    plt.title('Training and Validation Loss Over Epochs')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss (MSE)')
+    plt.legend()
+    plt.grid(True)
+    plt.savefig(os.path.join(output_dir, 'training_loss_plot.png'))
+    print(f"Loss plot saved to {os.path.join(output_dir, 'training_loss_plot.png')}")
+
+    loss_data = {
+        'train_loss_history': train_loss_history,
+        'val_loss_history': val_loss_history,
+        'val_epochs': val_epochs,
+        'lr_history': lr_history,
+        'best_val_loss': best_val_loss
+    }
+    with open(os.path.join(output_dir, 'loss_history.json'), 'w') as f:
+        json.dump(loss_data, f, indent=4)
+    print(f"Loss history saved to {os.path.join(output_dir, 'loss_history.json')}")
+    
+    script_end_time = time.time()
+    elapsed_time_seconds = script_end_time - script_start_time
+    print("="*60)
+    print(f"Total script execution time: {elapsed_time_seconds / 60:.2f} minutes ({elapsed_time_seconds:.2f} seconds)")
+if __name__ == '__main__':
+    main()
diff --git a/cleanroom/surrogateAI/time_series_train.py b/cleanroom/surrogateAI/time_series_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e4fdbf572bff5d01fcbcebf0f95fa0e937db3fb
--- /dev/null
+++ b/cleanroom/surrogateAI/time_series_train.py
@@ -0,0 +1,556 @@
+import os
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+from timm.models.layers import trunc_normal_
+from einops import rearrange
+import numpy as np
+from typing import List, Dict, Any
+import matplotlib.pyplot as plt
+import json
+import random
+import time
+from clean_eval import evaluate as rollout_evaluate
+
+# ============================================================================ 
+# 1. PYTORCH DATASET CLASS FOR TIME-STEPPED DATA
+# ============================================================================ 
+class TimeStepDataset(Dataset):
+    """Dataset for flattened timestep training data"""
+
+    def __init__(self, pt_file_path: str):
+        if not os.path.exists(pt_file_path):
+            raise FileNotFoundError(f"Dataset file not found at: {pt_file_path}")
+        self.data = torch.load(pt_file_path, weights_only=False)
+        print(f"Loaded {len(self.data)} timesteps from {pt_file_path}")
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, idx: int) -> Dict[str, Any]:
+        sample = self.data[idx]
+        return {
+            'coordinates': sample['coordinates'],
+            'node_type': sample['node_type'],
+            'current_velocities': sample['current_velocities'],
+            'target_velocities': sample['target_velocities'],
+            'meta_info': sample['meta_info']
+        }
+
+class TrajectoryDataset(Dataset):
+    """Dataset for full trajectory evaluation data"""
+
+    def __init__(self, pt_file_path: str):
+        if not os.path.exists(pt_file_path):
+            raise FileNotFoundError(f"Dataset file not found at: {pt_file_path}")
+        self.trajectories = torch.load(pt_file_path, weights_only=False)
+        print(f"Loaded {len(self.trajectories)} trajectories from {pt_file_path}")
+
+    def __len__(self) -> int:
+        return len(self.trajectories)
+
+    def __getitem__(self, idx: int) -> Dict[str, Any]:
+        return self.trajectories[idx]
+
+# ============================================================================ 
+# 2. TRANSOLVER MODEL ARCHITECTURE (Adapted for Time-Dependent Prediction)
+# ============================================================================ 
+ACTIVATION = {'gelu': nn.GELU, 'tanh': nn.Tanh, 'sigmoid': nn.Sigmoid, 'relu': nn.ReLU, 'leaky_relu': nn.LeakyReLU(0.1)}
+
+class MLP(nn.Module):
+    def __init__(self, n_input, n_hidden, n_output, n_layers=1, act='gelu', res=True):
+        super(MLP, self).__init__()
+        if act in ACTIVATION.keys():
+            act = ACTIVATION[act]
+        else:
+            raise NotImplementedError
+        self.linear_pre = nn.Sequential(nn.Linear(n_input, n_hidden), act())
+        self.linear_post = nn.Linear(n_hidden, n_output)
+        self.linears = nn.ModuleList([nn.Sequential(nn.Linear(n_hidden, n_hidden), act()) for _ in range(n_layers)])
+        self.res = res
+
+    def forward(self, x):
+        x = self.linear_pre(x)
+        for i in range(len(self.linears)):
+            if self.res:
+                x = self.linears[i](x) + x
+            else:
+                x = self.linears[i](x)
+        x = self.linear_post(x)
+        return x
+
+class Physics_Attention_Irregular_Mesh(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dropout=0., slice_num=64):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.dim_head = dim_head
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.softmax = nn.Softmax(dim=-1)
+        self.dropout = nn.Dropout(dropout)
+        self.temperature = nn.Parameter(torch.ones([1, heads, 1, 1]) * 0.5)
+        self.in_project_x = nn.Linear(dim, inner_dim)
+        self.in_project_fx = nn.Linear(dim, inner_dim)
+        self.in_project_slice = nn.Linear(dim_head, slice_num)
+        torch.nn.init.orthogonal_(self.in_project_slice.weight)
+        self.to_q = nn.Linear(dim_head, dim_head, bias=False)
+        self.to_k = nn.Linear(dim_head, dim_head, bias=False)
+        self.to_v = nn.Linear(dim_head, dim_head, bias=False)
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))
+
+    def forward(self, x):
+        B, N, C = x.shape
+        fx_mid = self.in_project_fx(x).reshape(B, N, self.heads, self.dim_head).permute(0, 2, 1, 3).contiguous()
+        x_mid = self.in_project_x(x).reshape(B, N, self.heads, self.dim_head).permute(0, 2, 1, 3).contiguous()
+        slice_weights = self.softmax(self.in_project_slice(x_mid) / self.temperature)
+        slice_norm = slice_weights.sum(2)
+        slice_token = torch.einsum("bhnc,bhng->bhgc", fx_mid, slice_weights)
+        slice_token = slice_token / ((slice_norm + 1e-5)[:, :, :, None].repeat(1, 1, 1, self.dim_head))
+        q_slice_token = self.to_q(slice_token)
+        k_slice_token = self.to_k(slice_token)
+        v_slice_token = self.to_v(slice_token)
+        dots = torch.matmul(q_slice_token, k_slice_token.transpose(-1, -2)) * self.scale
+        attn = self.softmax(dots)
+        attn = self.dropout(attn)
+        out_slice_token = torch.matmul(attn, v_slice_token)
+        out_x = torch.einsum("bhgc,bhng->bhnc", out_slice_token, slice_weights)
+        out_x = rearrange(out_x, 'b h n d -> b n (h d)')
+        out = self.to_out(out_x)
+        return out
+
+class Transolver_block(nn.Module):
+    def __init__(self, num_heads, hidden_dim, dropout, act='gelu', mlp_ratio=4, last_layer=False, out_dim=1, slice_num=32):
+        super().__init__()
+        self.last_layer = last_layer
+        self.ln_1 = nn.LayerNorm(hidden_dim)
+        self.Attn = Physics_Attention_Irregular_Mesh(hidden_dim, heads=num_heads, dim_head=hidden_dim // num_heads, dropout=dropout, slice_num=slice_num)
+        self.ln_2 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP(hidden_dim, hidden_dim * mlp_ratio, hidden_dim, n_layers=0, res=False, act=act)
+        if self.last_layer:
+            self.ln_3 = nn.LayerNorm(hidden_dim)
+            self.mlp2 = nn.Linear(hidden_dim, out_dim)
+
+    def forward(self, fx):
+        fx2 = self.Attn(self.ln_1(fx)) + fx
+        fx = self.mlp(self.ln_2(fx2)) + fx2
+        if self.last_layer:
+            return self.mlp2(self.ln_3(fx))
+        return fx
+
+class Model(nn.Module):
+    def __init__(self, in_dim=13, out_dim=3, n_layers=8, n_hidden=256, dropout=0, n_head=8, act='gelu', mlp_ratio=2, slice_num=32):
+        super(Model, self).__init__()
+        self.preprocess = MLP(in_dim, n_hidden * 2, n_hidden, n_layers=0, res=False, act=act)
+        self.n_hidden = n_hidden
+        self.blocks = nn.ModuleList([Transolver_block(num_heads=n_head, hidden_dim=n_hidden, dropout=dropout,
+                                                      act=act, mlp_ratio=mlp_ratio,
+                                                      out_dim=out_dim,
+                                                      slice_num=slice_num,
+                                                      last_layer=(_ == n_layers - 1))
+                                     for _ in range(n_layers)])
+        self.initialize_weights()
+        self.placeholder = nn.Parameter((1 / n_hidden) * torch.rand(n_hidden, dtype=torch.float))
+
+    def initialize_weights(self):
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, (nn.LayerNorm, nn.BatchNorm1d)):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x):
+        fx = self.preprocess(x)
+        fx = fx + self.placeholder[None, None, :] 
+        for block in self.blocks:
+            fx = block(fx)
+        return fx
+
+# ============================================================================ 
+# 3. CHECKPOINTING FUNCTIONS
+# ============================================================================ 
+def save_checkpoint(model, optimizer, scheduler, epoch, loss, path, norm_stats):
+    """Save training checkpoint"""
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'scheduler_state_dict': scheduler.state_dict() if scheduler else None,
+        'loss': loss,
+        'norm_stats': norm_stats  # Save normalization stats
+    }
+    torch.save(checkpoint, path)
+    print(f"Checkpoint saved: {path}")
+
+def load_checkpoint(model, optimizer, scheduler, path):
+    """Load training checkpoint"""
+    checkpoint = torch.load(path, map_location='cpu')
+    model.load_state_dict(checkpoint['model_state_dict'])
+    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+    if scheduler and checkpoint.get('scheduler_state_dict'):
+        scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+    
+    # Load norm_stats if they exist, otherwise return None
+    norm_stats = checkpoint.get('norm_stats', None)
+    if norm_stats is None:
+        print("Warning: Normalization stats not found in checkpoint.")
+        
+    return checkpoint['epoch'], checkpoint['loss'], norm_stats
+
+# ============================================================================ 
+# 4. EVALUATION FUNCTIONS
+# ============================================================================ 
+def create_wall_mask(node_type):
+    """Create mask to ignore wall nodes during loss calculation"""
+    wall_class_index = 5  # Assuming walls are class 5
+    node_class_indices = torch.argmax(node_type, dim=-1)
+    mask = (node_class_indices != wall_class_index).float().unsqueeze(-1)
+    return mask
+
+def evaluate_rollout(model, trajectory_loader, device, norm_stats):
+    """Run rollout evaluation on full trajectories"""
+    model.eval()
+    results = []
+    
+    print(f"Starting rollout evaluation on {len(trajectory_loader)} trajectories...")
+    
+    with torch.no_grad():
+        for i, trajectory in enumerate(trajectory_loader):
+            try:
+                print(f"\nProcessing trajectory {i+1}/{len(trajectory_loader)}")
+                
+                # Since trajectory_loader has batch_size=1, we need to extract the single trajectory
+                single_trajectory = {}
+                for key, value in trajectory.items():
+                    if key != 'meta_info':
+                        # Remove the batch dimension
+                        single_trajectory[key] = value.squeeze(0)
+                    else:
+                        # meta_info is special - it's a dict where each value is a list/tensor due to batching
+                        # We need to extract the first element from each value in the meta_info dict
+                        single_trajectory[key] = {}
+                        for meta_key, meta_value in value.items():
+                            if isinstance(meta_value, (list, tuple)):
+                                single_trajectory[key][meta_key] = meta_value[0]
+                            elif isinstance(meta_value, dict):
+                                # Handle nested dictionaries (like node_type_counts)
+                                single_trajectory[key][meta_key] = {}
+                                for nested_key, nested_value in meta_value.items():
+                                    if isinstance(nested_value, (list, tuple)):
+                                        single_trajectory[key][meta_key][nested_key] = nested_value[0]
+                                    elif torch.is_tensor(nested_value) and nested_value.numel() > 1:
+                                        single_trajectory[key][meta_key][nested_key] = nested_value[0]
+                                    else:
+                                        single_trajectory[key][meta_key][nested_key] = nested_value
+                            elif torch.is_tensor(meta_value) and meta_value.numel() > 1:
+                                # For tensors with more than one element, take the first element
+                                single_trajectory[key][meta_key] = meta_value[0]
+                            else:
+                                # For scalars or other types, keep as is
+                                single_trajectory[key][meta_key] = meta_value
+                
+                # Pass the normalization statistics to the evaluation function
+                _, traj_result = rollout_evaluate(model, single_trajectory, norm_stats)
+                results.append(traj_result)
+                print(f"Successfully processed trajectory {i+1}")
+                
+            except Exception as e:
+                print(f"Error processing trajectory {i+1}: {str(e)}")
+                print(f"Trajectory data shapes:")
+                for key, value in trajectory.items():
+                    if hasattr(value, 'shape'):
+                        print(f"  {key}: {value.shape}")
+                    else:
+                        print(f"  {key}: {type(value)} - {value}")
+                raise e
+                
+    print(f"Completed rollout evaluation on {len(results)} trajectories")
+    return results
+# ============================================================================ 
+# 5. MAIN TRAINING SCRIPT
+# ============================================================================ 
+def main():
+    script_start_time = time.time()
+    # --- CONFIGURATION ---
+    preprocessed_dir = r"/home/gd_user1/AnK/project_PINN/cleanroom/rawdataset"
+    base_filename = "final_data_timestep_data"
+    output_dir = r"/home/gd_user1/AnK/project_PINN/cleanroom/outputs_800"
+
+    train_pt_path = os.path.join(preprocessed_dir, f"{base_filename}_train.pt")
+    val_pt_path = os.path.join(preprocessed_dir, f"{base_filename}_test.pt")
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    hparams = {
+        'lr': 0.0005, 'batch_size': 4, 'nb_epochs': 800,
+        'in_dim': 13,  # coordinates(3) + node_type(6) + current_vel(3) + inlet_vel(1)
+        'out_dim': 3, 'n_hidden': 256, 'n_layers': 6, 'n_head': 8, 'slice_num': 32,
+        'checkpoint_freq': 10, 'eval_freq': 10
+    }
+
+    # --- SETUP ---
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Using device: {device}")
+    if torch.cuda.is_available():
+        print(f"GPU: {torch.cuda.get_device_name(0)}")
+        print(f"CUDA version: {torch.version.cuda}")
+
+    # Set random seeds for reproducibility
+    torch.manual_seed(42)
+    random.seed(42)
+    np.random.seed(42)
+
+    # --- DATALOADERS ---
+    print("Loading datasets...")
+    train_dataset = TimeStepDataset(train_pt_path)
+    val_timestep_dataset = TimeStepDataset(val_pt_path)  # For single-step validation
+    test_trajectory_dataset = TrajectoryDataset(val_pt_path)  # For rollout evaluation
+
+    train_loader = DataLoader(train_dataset, batch_size=hparams['batch_size'], shuffle=True)
+    val_timestep_loader = DataLoader(val_timestep_dataset, batch_size=hparams['batch_size'], shuffle=False)
+    test_trajectory_loader = DataLoader(test_trajectory_dataset, batch_size=1, shuffle=False)
+
+    # --- COMPUTE NORMALIZATION STATISTICS FROM TRAINING DATA ---
+    def compute_normalization_stats(train_dataset):
+        """Compute normalization statistics from training dataset"""
+        coords_list = []
+        vel_list = []
+        inlet_vel_list = []
+
+        print("Computing normalization statistics from training data...")
+
+        for sample in train_dataset:
+            coords_list.append(sample['coordinates'])
+            vel_list.append(sample['current_velocities'])
+            # Handle inlet velocity (it's in meta_info and may be tensor or scalar)
+            inlet_vel = sample['meta_info']['velocity']
+            if torch.is_tensor(inlet_vel):
+                inlet_vel_list.append(inlet_vel.item())
+            else:
+                inlet_vel_list.append(float(inlet_vel))
+
+        # Stack and compute statistics
+        coords_all = torch.stack(coords_list)  # [num_samples, num_nodes, 3]
+        vel_all = torch.stack(vel_list)        # [num_samples, num_nodes, 3]
+        inlet_vel_all = torch.tensor(inlet_vel_list)  # [num_samples]
+
+        stats = {
+            'coords_mean': coords_all.mean(dim=[0, 1]),  # Mean across samples and nodes
+            'coords_std': coords_all.std(dim=[0, 1]),
+            'vel_mean': vel_all.mean(dim=[0, 1]),
+            'vel_std': vel_all.std(dim=[0, 1]),
+            'inlet_vel_mean': inlet_vel_all.mean(),
+            'inlet_vel_std': inlet_vel_all.std()
+        }
+
+        print(f"Computed stats - Coords mean: {stats['coords_mean']}, std: {stats['coords_std']}")
+        print(f"Velocities mean: {stats['vel_mean']}, std: {stats['vel_std']}")
+        print(f"Inlet vel mean: {stats['inlet_vel_mean']:.6f}, std: {stats['inlet_vel_std']:.6f}")
+
+        return stats
+
+    # Compute normalization statistics
+    norm_stats = compute_normalization_stats(train_dataset)
+    coords_mean = norm_stats['coords_mean'].to(device)
+    coords_std = norm_stats['coords_std'].to(device)
+    vel_mean = norm_stats['vel_mean'].to(device)
+    vel_std = norm_stats['vel_std'].to(device)
+    inlet_vel_mean = norm_stats['inlet_vel_mean'].to(device)
+    inlet_vel_std = norm_stats['inlet_vel_std'].to(device)
+    epsilon = 1e-8  # Small constant to prevent division by zero
+
+    # --- MODEL, OPTIMIZER, SCHEDULER SETUP ---
+    model = Model(in_dim=hparams['in_dim'], out_dim=hparams['out_dim'], 
+                  n_hidden=hparams['n_hidden'], n_layers=hparams['n_layers'],
+                  n_head=hparams['n_head'], slice_num=hparams['slice_num']).to(device)
+    
+    optimizer = torch.optim.Adam(model.parameters(), lr=hparams['lr'])
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
+    optimizer,
+    T_0=50,      # Number of epochs for the first restart cycle
+    T_mult=2,    # Factor to increase T_0 after each restart (2x longer each time)
+    eta_min=1e-7 # Minimum learning rate
+)
+    criterion = nn.MSELoss()
+
+    # Initialize training variables
+    start_epoch = 0
+    best_val_loss = float('inf')
+
+    # Check for existing checkpoint to resume training
+    latest_checkpoint_path = os.path.join(output_dir, 'checkpoint_latest.pth')
+    if os.path.exists(latest_checkpoint_path):
+        print(f"Found existing checkpoint: {latest_checkpoint_path}")
+        try:
+            start_epoch, last_loss, loaded_norm_stats = load_checkpoint(
+                model, optimizer, scheduler, latest_checkpoint_path
+            )
+            if loaded_norm_stats is not None:
+                norm_stats = loaded_norm_stats
+                # Update the normalization tensors
+                coords_mean = norm_stats['coords_mean'].to(device)
+                coords_std = norm_stats['coords_std'].to(device)
+                vel_mean = norm_stats['vel_mean'].to(device)
+                vel_std = norm_stats['vel_std'].to(device)
+                inlet_vel_mean = norm_stats['inlet_vel_mean'].to(device)
+                inlet_vel_std = norm_stats['inlet_vel_std'].to(device)
+                print("Loaded normalization statistics from checkpoint.")
+            print(f"Resuming training from epoch {start_epoch}")
+            best_val_loss = last_loss
+        except Exception as e:
+            print(f"Error loading checkpoint: {e}")
+            print("Starting fresh training...")
+            start_epoch = 0
+            best_val_loss = float('inf')
+
+    print(f"Starting training from epoch {start_epoch} to {hparams['nb_epochs']}")
+    print("="*60)
+
+    train_loss_history = []
+    val_loss_history = []
+    lr_history = []
+    val_epochs = []
+
+    # --- TRAINING LOOP ---
+    for epoch in range(start_epoch, hparams['nb_epochs']):
+        epoch_start_time = time.time()
+        model.train()
+        total_train_loss = 0.0
+
+        for batch_idx, batch in enumerate(train_loader):
+            coordinates = batch['coordinates'].to(device)
+            node_type = batch['node_type'].to(device)
+            current_velocities = batch['current_velocities'].to(device)
+            target_velocities = batch['target_velocities'].to(device)
+
+            inlet_velocities = batch['meta_info']['velocity']
+            if torch.is_tensor(inlet_velocities):
+                inlet_vel_tensor = inlet_velocities.clone().detach().float().to(device)
+            else:
+                inlet_vel_tensor = torch.tensor(inlet_velocities, dtype=torch.float32).to(device)
+            
+            # --- NORMALIZE FEATURES ---
+            norm_coords = (coordinates - coords_mean) / (coords_std + epsilon)
+            norm_current_vel = (current_velocities - vel_mean) / (vel_std + epsilon)
+            norm_target_vel = (target_velocities - vel_mean) / (vel_std + epsilon)
+            norm_inlet_vel = (inlet_vel_tensor - inlet_vel_mean) / (inlet_vel_std + epsilon)
+
+            batch_size, num_nodes, _ = coordinates.shape
+            inlet_vel_feature = norm_inlet_vel.view(batch_size, 1, 1).expand(batch_size, num_nodes, 1)
+
+            # Input: [normalized coordinates, node_type, normalized velocities, normalized inlet_velocity]
+            input_features = torch.cat([norm_coords, node_type, norm_current_vel, inlet_vel_feature], dim=-1)
+
+            optimizer.zero_grad()
+            predicted_normalized_velocities = model(input_features)
+
+            # Apply wall masking
+            mask = create_wall_mask(node_type)
+            loss = criterion(predicted_normalized_velocities * mask, norm_target_vel * mask)
+            loss.backward()
+            optimizer.step()
+
+            total_train_loss += loss.item()
+
+            # Progress update every 50 batches
+            if batch_idx % 50 == 0:
+                print(f"Epoch {epoch+1}/{hparams['nb_epochs']} | Batch {batch_idx+1}/{len(train_loader)} | Loss: {loss.item():.6f}")
+
+        scheduler.step()
+        lr_history.append(optimizer.param_groups[0]['lr'])
+        epoch_time = time.time() - epoch_start_time
+        avg_train_loss = total_train_loss / len(train_loader)
+        train_loss_history.append(avg_train_loss)
+
+        print(f"Epoch {epoch+1}/{hparams['nb_epochs']} completed | Avg Train Loss: {avg_train_loss:.6f} | Time: {epoch_time:.2f}s")
+
+        # --- PERIODIC EVALUATION ---
+        if (epoch + 1) % hparams['eval_freq'] == 0 or epoch == hparams['nb_epochs'] - 1:
+            print(f"Epoch {epoch+1}/{hparams['nb_epochs']} | Train Loss: {avg_train_loss:.6f}")
+
+            # Rollout evaluation
+            print("  Running rollout evaluation...")
+            rollout_results = evaluate_rollout(model, test_trajectory_loader, device, norm_stats)
+
+            # Calculate loss from rollout
+            total_rollout_mse = 0.0
+            for result in rollout_results:
+                # Move tensors to the correct device
+                pred_traj = result['predicted_trajectory'].to(device)
+                gt_traj = result['ground_truth_trajectory'].to(device)
+                node_type = result['node_type'].to(device)
+
+                # Create a mask to ignore wall nodes
+                mask = create_wall_mask(node_type).unsqueeze(0)  # Add time dim for broadcasting
+
+                # Calculate masked MSE
+                loss = criterion(pred_traj * mask, gt_traj * mask)
+                total_rollout_mse += loss.item()
+            
+            avg_rollout_mse = total_rollout_mse / len(rollout_results)
+            
+            val_loss = avg_rollout_mse
+            val_loss_history.append(val_loss)
+            val_epochs.append(epoch + 1)
+            print(f"  Validation Rollout MSE: {val_loss:.6f}")
+
+            # Save rollout results
+            rollout_path = os.path.join(output_dir, f"rollout_results_epoch_{epoch+1}.npy")
+            np.save(rollout_path, rollout_results, allow_pickle=True) # allow_pickle is good practice for np.save with dicts
+            print(f"  -> Saved rollout results to {rollout_path}")
+
+            # Save best model based on rollout loss
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                best_model_path = os.path.join(output_dir, 'best_model.pth')
+                torch.save(model.state_dict(), best_model_path)
+                print(f"  -> New best model saved with validation loss: {best_val_loss:.6f}")
+
+        # --- PERIODIC CHECKPOINTING ---
+        if (epoch + 1) % hparams['checkpoint_freq'] == 0:
+            # Use validation loss if available, otherwise use training loss
+            checkpoint_loss = val_loss if 'val_loss' in locals() else avg_train_loss
+            checkpoint_path = os.path.join(output_dir, f'checkpoint_epoch_{epoch+1}.pth')
+            save_checkpoint(model, optimizer, scheduler, epoch + 1, checkpoint_loss, checkpoint_path, norm_stats)
+
+    # --- SAVE LATEST CHECKPOINT ---
+    save_checkpoint(model, optimizer, scheduler, hparams['nb_epochs'], best_val_loss,
+                   os.path.join(output_dir, 'checkpoint_latest.pth'), norm_stats)
+
+    print("="*60)
+    print("Training complete.")
+    print(f"Best validation loss: {best_val_loss:.6f}")
+
+    # --- PLOTTING AND SAVING LOSS HISTORY ---
+    plt.figure(figsize=(10, 5))
+    plt.plot(train_loss_history, label='Training Loss')
+    plt.plot(val_epochs, val_loss_history, 'o-', label='Validation Loss')
+    plt.title('Training and Validation Loss Over Epochs')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss (MSE)')
+    plt.legend()
+    plt.grid(True)
+    plt.savefig(os.path.join(output_dir, 'training_loss_plot.png'))
+    print(f"Loss plot saved to {os.path.join(output_dir, 'training_loss_plot.png')}")
+
+    loss_data = {
+        'train_loss_history': train_loss_history,
+        'val_loss_history': val_loss_history,
+        'val_epochs': val_epochs,
+        'lr_history': lr_history,
+        'best_val_loss': best_val_loss
+    }
+    with open(os.path.join(output_dir, 'loss_history.json'), 'w') as f:
+        json.dump(loss_data, f, indent=4)
+    print(f"Loss history saved to {os.path.join(output_dir, 'loss_history.json')}")
+    
+    script_end_time = time.time()
+    elapsed_time_seconds = script_end_time - script_start_time
+    print("="*60)
+    print(f"Total script execution time: {elapsed_time_seconds / 60:.2f} minutes ({elapsed_time_seconds:.2f} seconds)")
+if __name__ == '__main__':
+    main()
diff --git a/cleanroom/test_files/read_pt.py b/cleanroom/test_files/read_pt.py
new file mode 100644
index 0000000000000000000000000000000000000000..563de90e6bb5f02810cdd84a887f7794bf70e5ba
--- /dev/null
+++ b/cleanroom/test_files/read_pt.py
@@ -0,0 +1,23 @@
+import torch
+
+file_path = "/home/gd_user1/AnK/project_PINN/cleanroom/rawdataset/final_data_timestep_data_train.pt"
+
+
+print(f"Reading data from '{file_path}'...")
+
+try:
+    # Use torch.load() to read the .pt file.
+    # This function deserializes the pickled object file and loads it into memory.
+    loaded_tensor = torch.load(file_path)
+
+    print("Successfully loaded the tensor. Here is the content:")
+    print(loaded_tensor)
+
+    # You can now work with the loaded data
+    print("\nType of the loaded data:", type(loaded_tensor))
+    print("Shape of the loaded tensor:", loaded_tensor.shape)
+
+except FileNotFoundError:
+    print(f"Error: The file '{file_path}' was not found.")
+except Exception as e:
+    print(f"An error occurred while reading the file: {e}")
\ No newline at end of file