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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+results/test_A1_my.png filter=lfs diff=lfs merge=lfs -text
+results/test_A2_h.png filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,118 @@
+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- computational-fluid-dynamics
+- shallow-water-equations
+frameworks: PyTorch
+---
+<p align="center"><strong><span style="font-size: 30px;">FluxNet-ShallowWater-LAP</span></strong></p>
+
+# Model Introduction
+
+FluxNet-ShallowWater-LAP reproduces the two-dimensional shallow-water FluxNet-LAP pushforward experiment in the [FluxNet paper](https://arxiv.org/pdf/2602.01941). It forecasts water depth and two momentum fields on a 64x64 grid.
+
+# Model Description
+
+The model takes `(h, mx, my)` and predicts the next state. The configuration uses 64 channels, six residual blocks, 5x5 kernels, a 3x3 transport neighborhood and five-step pushforward training.
+
+## Use Cases
+
+| Use case | Description |
+| --- | --- |
+| Shallow-water forecasting | Forecast depth and momentum on a regular grid |
+| Simulation acceleration | Compare surrogate rollouts against numerical reference trajectories |
+
+# Usage
+
+## 1. OneCode
+
+[Open OneCode](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
+
+## 2. Manual Setup
+
+**Hardware requirements**
+
+- Training and inference require a Slurm-allocated HIP/DCU compute node; there is no silent CPU fallback.
+- The formal run used one DCU, eight CPU cores and 30 GB of host memory. GPU execution was not validated.
+
+### Download the Model
+
+```bash
+hf download OneScience-Group/FluxNet-ShallowWater-LAP --local-dir ./FluxNet-ShallowWater-LAP
+cd FluxNet-ShallowWater-LAP
+```
+
+### Install Dependencies
+
+**DCU environment**
+
+```bash
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+pip install numpy h5py PyYAML matplotlib
+```
+
+**GPU environment**
+
+```bash
+conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
+conda activate onescience311
+pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+pip install numpy h5py PyYAML matplotlib
+```
+
+The GPU commands describe dependency installation only; the provided scripts were validated on Slurm HIP/DCU.
+
+### Training Data
+
+The [OneScience/FluxNet-ShallowWater dataset on ModelScope](https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater) contains 50 training, 20 validation, 50 short-test and 50 long-test trajectories produced with the paper's official numerical generator. Each HDF5 trajectory contains `h`, `mx`, `my` and `t`. The dataset is not mirrored on Hugging Face. Download it into the model directory:
+
+```bash
+modelscope download --dataset OneScience/FluxNet-ShallowWater --local_dir ./data
+```
+
+Run commands from the package root. `data.root` in `config/config.yaml` defaults to `./data`.
+
+### Training
+
+On a Slurm-allocated DCU node:
+
+```bash
+python scripts/train.py
+```
+
+The full run trains for 300 epochs and writes `weight/best_model.pth`. Back up the included `best_model.pth` and `last_model.pth` before retraining if they must be preserved.
+
+### Trained Weights
+
+The included checkpoint is the best validation-MSE model at epoch 283 of a completed 300-epoch run. `weight/best_model.pth` is 15636928 bytes; SHA256: `47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70`.
+
+### Inference
+
+```bash
+python scripts/inference.py
+```
+
+### Evaluation and Visualization
+
+```bash
+python scripts/result.py
+```
+
+Outputs are written to `results/`. The long-horizon (4.8 time units) MAE for `(h, mx, my)` is `(1.4063e-3, 1.8558e-3, 1.7632e-3)`; the paper's Table 3 two-times extrapolation means are `(1.75e-3, 2.38e-3, 2.30e-3)`. Our result is one seed, not a reproduction of the paper's multi-seed mean and standard deviation.
+
+# OneScience Repositories
+
+| Platform | Main repository | Skills repository |
+| --- | --- | --- |
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+# Citation and License
+
+- Paper: [FluxNet](https://arxiv.org/pdf/2602.01941).
+- [Original public repository](https://github.com/Lan-zs/FluxNet); [public dataset and its provenance](https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater). This model package is an independent reproduction.

config.json

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+{
+  "model_type": "fluxnet_shallow_water_lap",
+  "architectures": ["FluxNetShallowWater"],
+  "task": "two-dimensional shallow-water rollout",
+  "framework": "pytorch",
+  "model_file": "model/FluxNetShallowWater.py",
+  "checkpoint": "weight/best_model.pth",
+  "config_file": "config/config.yaml",
+  "dataset_url": "https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater",
+  "paper_url": "https://arxiv.org/pdf/2602.01941"
+}

config/config.yaml

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+experiment: FluxNet-ShallowWater-LAP-pushforward
+paper: https://arxiv.org/pdf/2602.01941v2
+seed: 42
+data:
+  root: ./data
+  fields: [h, mx, my]
+  shape: [64, 64]
+  dt: 0.04
+  expected_counts: {train: 50, val: 20, test: 50, test_long: 50}
+  expected_frames: {train: 61, val: 61, test: 61, test_long: 121}
+  normalization: none
+  reference_negative_tolerance: 1.0e-9
+model:
+  base_channels: 64
+  num_blocks: 6
+  kernel_size: 5
+  neighborhood_size: 3
+  epsilon: 1.0e-6
+training:
+  epochs: 300
+  pushforward_steps: 5
+  batch_size: 16
+  num_workers: 4
+  optimizer: AdamW
+  learning_rate: 1.0e-3
+  weight_decay: 1.0e-2
+  scheduler: ReduceLROnPlateau
+  scheduler_factor: 0.5
+  scheduler_patience: 15
+  loss: mse_step1_plus_mse_step5
+  early_stopping: false
+  gradient_clip: null
+  precision: float32
+  print_every_batches: 10
+  checkpoint_every_epochs: 10
+evaluation:
+  batch_size: 10
+  long_test_every_epochs: 50
+  splits: [test, test_long]
+  example_cases: [A1, A2, B1, B2]
+paths:
+  best_checkpoint: weight/best_model.pth
+  last_checkpoint: weight/last_model.pth
+  results: results

model/FluxNetShallowWater.py

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+"""Independent implementation of FluxNet-LAP (arXiv:2602.01941v2).
+
+The transport equations and epsilon follow Sections 3.2, 3.3 and 3.5;
+the backbone dimensions follow Appendix C.2. See reproduction_spec.md
+for the explicitly identified gaps filled by read-only official references.
+"""
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+class FeatureBlock(nn.Module):
+    """Two periodic convolutions, with a learned concatenation skip fusion."""
+
+    def __init__(self, width, kernel_size):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Conv2d(width, width, kernel_size, padding=kernel_size // 2,
+                      padding_mode="circular"),
+            nn.BatchNorm2d(width), nn.GELU(),
+            nn.Conv2d(width, width, kernel_size, padding=kernel_size // 2,
+                      padding_mode="circular"),
+            nn.BatchNorm2d(width), nn.GELU(),
+        )
+        self.fuse = nn.Conv2d(2 * width, width, 1)
+
+    def forward(self, features):
+        return self.fuse(torch.cat((self.layers(features), features), dim=1))
+
+
+class FluxNetShallowWater(nn.Module):
+    """Map physical states [B,3,H,W] ordered h,mx,my to the next state.
+
+    This operator redistributes cumulative amounts, with no dt multiplier,
+    clipping, mass projection, or numerical-solver substeps.
+    """
+
+    def __init__(self, base_channels=64, num_blocks=6, kernel_size=5,
+                 neighborhood_size=3, epsilon=1e-6):
+        super().__init__()
+        if kernel_size % 2 != 1 or neighborhood_size % 2 != 1:
+            raise ValueError("Convolution and transport sizes must be odd")
+        if neighborhood_size < 3 or epsilon <= 0:
+            raise ValueError("Invalid transport size or epsilon")
+        self.epsilon = float(epsilon)
+        radius = neighborhood_size // 2
+        self.offsets = tuple((dy, dx) for dy in range(-radius, radius + 1)
+                             for dx in range(-radius, radius + 1)
+                             if (dy, dx) != (0, 0))
+        self.encoder = nn.Sequential(
+            nn.Conv2d(3, base_channels, kernel_size, padding=kernel_size // 2,
+                      padding_mode="circular"),
+            nn.BatchNorm2d(base_channels), nn.GELU(),
+            *(FeatureBlock(base_channels, kernel_size) for _ in range(num_blocks)),
+        )
+        directions = len(self.offsets)
+        self.depth_head = nn.Conv2d(base_channels, directions + 1, 1)
+        self.pressure_x = nn.Conv2d(base_channels, directions, 1)
+        self.pressure_y = nn.Conv2d(base_channels, directions, 1)
+
+    def transport(self, state):
+        features = self.encoder(state)
+        h = state[:, :1]
+        raw = self.depth_head(features)
+        outgoing_depth = h * raw[:, :1].sigmoid()
+        depth_transfers = outgoing_depth * raw[:, 1:].softmax(dim=1)
+        velocity = state[:, 1:] / (h + self.epsilon)
+        gate = h.square()
+        momentum_x = depth_transfers * velocity[:, :1] + gate * F.softplus(self.pressure_x(features))
+        momentum_y = depth_transfers * velocity[:, 1:] + gate * F.softplus(self.pressure_y(features))
+        transfers = torch.stack((depth_transfers, momentum_x, momentum_y), dim=1)
+        return outgoing_depth, transfers
+
+    def forward(self, state):
+        outgoing_depth, transfers = self.transport(state)
+        # For h, use the analytically summed alpha*h to retain the lower bound
+        # in floating point. Both forms are algebraically the same (Prop. 2).
+        outgoing = torch.cat((outgoing_depth, transfers[:, 1:].sum(dim=2)), dim=1)
+        updated = state - outgoing
+        for index, offset in enumerate(self.offsets):
+            # Offset denotes receiver minus sender; rolling moves sender's
+            # outgoing field to its receiver on the periodic torus.
+            updated = updated + torch.roll(transfers[:, :, index], offset, dims=(-2, -1))
+        return updated

model/__init__.py

@@ -0,0 +1,3 @@
+from .FluxNetShallowWater import FluxNetShallowWater
+
+__all__ = ["FluxNetShallowWater"]

requirements.txt

@@ -0,0 +1,5 @@
+torch
+numpy
+h5py
+PyYAML
+matplotlib

results/inference_manifest.json

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+}

results/metrics.json

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results/report.md

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+# FluxNet shallow-water LAP pushforward reproduction
+
+Source: https://arxiv.org/pdf/2602.01941v2 (Sections 3.2–3.7, Appendix C.2).
+
+One independent training run (seed 42), 300 complete epochs. This is not a five-seed mean/std replication.
+64 channels, 6 residual blocks, kernel 5, transport stencil 3×3, detached pushforward length 5.
+AdamW lr=1e-3, weight_decay=1e-2; ReduceLROnPlateau factor=0.5, patience=15; float32 states.
+
+Selected checkpoint epoch: 283; validation MSE: 1.11785405e-06.
+Checkpoint SHA256: `47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70`.
+
+| Split | Horizon | MAE h | MAE mx | MAE my | Negative h (%) |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| test | 2.4 | 1.30214413e-03 | 1.79798426e-03 | 1.72321738e-03 | 0.00000000e+00 |
+| test_long | 4.8 | 1.40631581e-03 | 1.85576825e-03 | 1.76324718e-03 | 0.00000000e+00 |
+
+MAE averages all forecast steps, grid cells, and 50 trajectories, excluding the initial frame.
+Final-time and T=2/T=2.4/T=4.8 prefix metrics are separately stored in metrics.json.
+The paper labels Table 3 as T=2 while Appendix C.1 specifies 2.4/4.8; both time conventions are retained.
+
+Conservation is computed using float64 reductions of float32 predictions relative to the initial state.
+Relative drift divides by |initial signed total|+1e-8; zero-total momentum makes this statistic ill-conditioned.
+Absolute sum drift and initial-L1-scaled drift are therefore also reported. No postprocessing correction is used.
+
+test maximum absolute sum drift [h,mx,my]: [2.2027743398211896e-05, 3.510061651468277e-05, 3.0668867111671716e-05]
+test maximum relative drift [h,mx,my]: [2.168669351450529e-08, 3510.061651468277, 2132.3561668395996]
+test minimum depth: 0.00000000e+00
+
+test_long maximum absolute sum drift [h,mx,my]: [2.8165319235995412e-05, 4.894798621535301e-05, 3.981318513979204e-05]
+test_long maximum relative drift [h,mx,my]: [2.574266788559366e-08, 4894.798621535301, 2548.028714954853]
+test_long minimum depth: 0.00000000e+00
+
+All arrays, checkpoints, training history, and saved-case MAE recomputations were checked.
+The original numerical-generator datasets were read directly; no data generation or official repository code was downloaded.
+
+Reference data contains small negative roundoff depths (about 1e-11); these are retained unchanged.
+Data validation tolerates reference values down to -1e-9; model violation metrics use the strict condition h<0, with no tolerance or clipping.

results/test.json

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results/training_status.json

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results/verification.json

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+    "test_long": {
+      "all_trajectories": true,
+      "all_steps": true,
+      "finite": true,
+      "metrics_recomputed": true,
+      "example_fields_recomputed": true,
+      "zero_negative_depth": true
+    }
+  },
+  "figures": [
+    "training_curve.png",
+    "test_rollout_metrics.png",
+    "test_A1_h.png",
+    "test_A1_mx.png",
+    "test_A1_my.png",
+    "test_A2_h.png",
+    "test_A2_mx.png",
+    "test_A2_my.png",
+    "test_B1_h.png",
+    "test_B1_mx.png",
+    "test_B1_my.png",
+    "test_B2_h.png",
+    "test_B2_mx.png",
+    "test_B2_my.png",
+    "test_long_rollout_metrics.png",
+    "test_long_A1_h.png",
+    "test_long_A1_mx.png",
+    "test_long_A1_my.png",
+    "test_long_A2_h.png",
+    "test_long_A2_mx.png",
+    "test_long_A2_my.png",
+    "test_long_B1_h.png",
+    "test_long_B1_mx.png",
+    "test_long_B1_my.png",
+    "test_long_B2_h.png",
+    "test_long_B2_mx.png",
+    "test_long_B2_my.png"
+  ],
+  "report": "report.md"
+}

scripts/common.py

@@ -0,0 +1,271 @@
+"""Data, provenance, and metrics shared by the independent reproduction."""
+import argparse
+import hashlib
+import json
+import os
+import platform
+import random
+import sys
+from pathlib import Path
+
+import h5py
+import numpy as np
+import torch
+import yaml
+from torch.utils.data import Dataset
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+from model import FluxNetShallowWater
+
+FIELDS = ("h", "mx", "my")
+
+
+def load_config(path=None):
+    with open(path or ROOT / "config/config.yaml", encoding="utf-8") as stream:
+        cfg = yaml.safe_load(stream)
+    for name, expected in {"epochs": 300, "pushforward_steps": 5}.items():
+        if cfg["training"][name] != expected:
+            raise ValueError(f"Formal experiment requires {name}={expected}")
+    if cfg["training"]["early_stopping"] or cfg["data"]["normalization"] != "none":
+        raise ValueError("Formal run requires 300 full epochs and physical states")
+    return cfg
+
+
+def checkpoint_config_matches(saved, current):
+    # The published dataset can be relocated; all experiment settings must still match.
+    saved = {**saved, "data": {**saved["data"], "root": None}}
+    current = {**current, "data": {**current["data"], "root": None}}
+    return saved == current
+
+
+def setup(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.set_num_threads(min(8, int(os.environ.get("SLURM_CPUS_PER_TASK", 8))))
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+
+
+def device():
+    if not os.environ.get("SLURM_JOB_ID"):
+        raise RuntimeError("Run training/inference inside a SCNet DCU SLURM allocation")
+    if not torch.cuda.is_available() or not torch.version.hip:
+        raise RuntimeError("A HIP/DCU PyTorch device is required; CPU fallback is disabled")
+    return torch.device("cuda")
+
+
+def output_dir(cfg):
+    path = ROOT / cfg["paths"]["results"]
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def save_json(path, obj):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + ".tmp")
+    temporary.write_text(json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) + "\n")
+    temporary.replace(path)
+
+
+def sha256(path):
+    digest = hashlib.sha256()
+    with open(path, "rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def provenance():
+    return {"hostname": platform.node(), "slurm_job_id": os.environ.get("SLURM_JOB_ID"),
+            "torch": torch.__version__, "hip": torch.version.hip,
+            "python": sys.version, "device": torch.cuda.get_device_name(),
+            "device_count": torch.cuda.device_count(),
+            "source_sha256": {str(p.relative_to(ROOT)): sha256(p)
+                              for p in sorted(ROOT.rglob("*.py")) if "__pycache__" not in str(p)}}
+
+
+class Trajectories:
+    def __init__(self, cfg, split, validate=True):
+        self.split = split
+        self.files = sorted((Path(cfg["data"]["root"]) / split).glob("*.h5"))
+        if len(self.files) != cfg["data"]["expected_counts"][split]:
+            raise ValueError(f"Unexpected {split} trajectory count: {len(self.files)}")
+        self.values = []
+        self.metadata = []
+        expected = (cfg["data"]["expected_frames"][split], *cfg["data"]["shape"])
+        for path in self.files:
+            with h5py.File(path, "r") as source:
+                fields = []
+                for field in FIELDS:
+                    array = source[field][...]
+                    if array.dtype != np.float32 or array.shape != expected:
+                        raise ValueError(f"{path}:{field}: {array.dtype} {array.shape}")
+                    if validate and not np.isfinite(array).all():
+                        raise ValueError(f"Nonfinite data: {path}:{field}")
+                    fields.append(array)
+                state = np.stack(fields, axis=1)
+                if validate and state[:, 0].min() < -cfg["data"]["reference_negative_tolerance"]:
+                    raise ValueError(f"Substantive negative reference depth: {path}")
+                if validate and state[0, 0].min() < 0:
+                    raise ValueError(f"Negative initial reference depth: {path}")
+                times = source["t"][...].astype(np.float64)
+                if not np.allclose(np.diff(times), cfg["data"]["dt"], atol=2e-6):
+                    raise ValueError(f"Unexpected snapshot interval: {path}")
+                self.times = times
+                self.metadata.append({"file": str(path), "bytes": path.stat().st_size,
+                                      "frames": len(times), "t_start": float(times[0]),
+                                      "t_end": float(times[-1]), "min_h": float(state[:, 0].min()),
+                                      "reference_negative_h_count": int((state[:, 0] < 0).sum()),
+                                      "min": state.min(axis=(0, 2, 3)).tolist(),
+                                      "max": state.max(axis=(0, 2, 3)).tolist()})
+                self.values.append(torch.from_numpy(state))
+        self.values = torch.stack(self.values)
+        reference_negatives = sum(m["reference_negative_h_count"] for m in self.metadata)
+        print(f"DATA {split}: {tuple(self.values.shape)}; dt={cfg['data']['dt']} "
+              f"t_end={self.times[-1]:.2f}; normalization=none; "
+              f"raw_reference_negative_cells={reference_negatives} (roundoff retained, no clipping)", flush=True)
+
+
+class Windows(Dataset):
+    def __init__(self, trajectories, steps):
+        self.trajectories = trajectories
+        self.steps = steps
+        self.starts = trajectories.values.shape[1] - steps
+
+    def __len__(self):
+        return self.trajectories.values.shape[0] * self.starts
+
+    def __getitem__(self, index):
+        trajectory, start = divmod(index, self.starts)
+        return self.trajectories.values[trajectory, start:start + self.steps + 1]
+
+
+def build_model(cfg, target_device):
+    return FluxNetShallowWater(**cfg["model"]).to(target_device)
+
+
+@torch.no_grad()
+def validate(model, loader, target_device):
+    model.eval()
+    square = torch.zeros(3, device=target_device, dtype=torch.float64)
+    absolute = torch.zeros_like(square)
+    count = 0
+    negative = 0
+    terminal_square = torch.zeros_like(square)
+    for window in loader:
+        window = window.to(target_device, non_blocking=True)
+        prediction = model(window[:, 0])
+        error = prediction.double() - window[:, 1].double()
+        square += error.square().sum(dim=(0, 2, 3))
+        absolute += error.abs().sum(dim=(0, 2, 3))
+        current = prediction
+        for _ in range(1, window.shape[1] - 1):
+            current = model(current)
+        terminal_error = current.double() - window[:, -1].double()
+        terminal_square += terminal_error.square().sum(dim=(0, 2, 3))
+        count += prediction.shape[0] * prediction.shape[2] * prediction.shape[3]
+        negative += int((prediction[:, 0] < 0).sum())
+    return {"mse": float(((square + terminal_square) / count).mean()),
+            "step1_mse": float((square / count).mean()),
+            "step5_mse": float((terminal_square / count).mean()),
+            "mae": (absolute / count).tolist(), "negative_h_ratio": negative / count}
+
+
+@torch.no_grad()
+def rollout_metrics(model, trajectories, target_device, batch_size=10,
+                    save_path=None, example_cases=("A1", "A2", "B1", "B2")):
+    model.eval()
+    values = trajectories.values
+    n, frames, channels, height, width = values.shape
+    steps = frames - 1
+    mae = np.empty((n, steps, channels), dtype=np.float64)
+    cons_relative = np.empty_like(mae)
+    cons_abs = np.empty_like(mae)
+    cons_scaled = np.empty_like(mae)
+    negatives = np.empty((n, steps), dtype=np.float64)
+    minima = np.empty((n, steps), dtype=np.float64)
+    examples = {}
+    example_indices = {}
+    for case in example_cases:
+        matches = [i for i, p in enumerate(trajectories.files) if p.name.startswith("Case" + case + "_")]
+        if matches:
+            example_indices[matches[0]] = case
+    for begin in range(0, n, batch_size):
+        end = min(begin + batch_size, n)
+        current = values[begin:end, 0].to(target_device)
+        initial_sum = current.double().sum(dim=(-2, -1))
+        initial_abs_sum = current.double().abs().sum(dim=(-2, -1))
+        traces = []
+        keep = any(begin <= idx < end for idx in example_indices)
+        if keep:
+            traces.append(current.cpu().numpy())
+        for step in range(steps):
+            current = model(current)
+            if not torch.isfinite(current).all():
+                raise FloatingPointError(f"Nonfinite {trajectories.split} rollout at step {step + 1}")
+            target = values[begin:end, step + 1].to(target_device)
+            mae[begin:end, step] = (current.double() - target.double()).abs().mean((-2, -1)).cpu().numpy()
+            drift = (current.double().sum((-2, -1)) - initial_sum).abs()
+            cons_abs[begin:end, step] = drift.cpu().numpy()
+            # Paper's signed-total denominator is ill-conditioned for zero-net
+            # momentum; preserve it and also report absolute and L1-scaled drift.
+            cons_relative[begin:end, step] = (drift / (initial_sum.abs() + 1e-8)).cpu().numpy()
+            cons_scaled[begin:end, step] = (drift / (initial_abs_sum + 1e-8)).cpu().numpy()
+            negatives[begin:end, step] = (current[:, 0] < 0).double().mean((-2, -1)).cpu().numpy()
+            minima[begin:end, step] = current[:, 0].amin((-2, -1)).cpu().numpy()
+            if keep:
+                traces.append(current.cpu().numpy())
+        if keep:
+            traces = np.stack(traces, axis=1)
+            for idx, case in example_indices.items():
+                if begin <= idx < end:
+                    examples[case + "_prediction"] = traces[idx - begin]
+                    examples[case + "_target"] = values[idx].numpy()
+                    examples[case + "_file"] = np.array(trajectories.files[idx].name)
+        print(f"ROLLOUT {trajectories.split} {end}/{n}: "
+              f"MAE={mae[:end].mean((0, 1)).tolist()} "
+              f"cons_abs_max={cons_abs[:end].max((0, 1)).tolist()} "
+              f"negative_h_ratio={negatives[:end].mean():.6e}", flush=True)
+    summary = {"split": trajectories.split, "trajectories": n, "steps": steps,
+               "t_end": float(trajectories.times[-1]), "fields": list(FIELDS),
+               "mae": mae.mean((0, 1)).tolist(),
+               "mae_std_across_trajectories": mae.mean(1).std(0).tolist(),
+               "mae_final": mae[:, -1].mean(0).tolist(),
+               "conservation_relative_mean": cons_relative.mean((0, 1)).tolist(),
+               "conservation_relative_max": cons_relative.max((0, 1)).tolist(),
+               "conservation_absolute_sum_mean": cons_abs.mean((0, 1)).tolist(),
+               "conservation_absolute_sum_max": cons_abs.max((0, 1)).tolist(),
+               "conservation_initial_l1_scaled_max": cons_scaled.max((0, 1)).tolist(),
+               "negative_h_ratio": float(negatives.mean()),
+               "negative_h_percent": float(100 * negatives.mean()),
+               "min_h": float(minima.min()),
+               "reduction": "all trajectories, forecast steps 1..T, all grid cells; initial frame excluded",
+               "accumulation_dtype": "float64 (model states float32)",
+               "zero_momentum_note": "Relative drift uses |sum(m0)|+1e-8; inspect absolute drift and initial-L1-scaled drift for zero-total cases."}
+    for horizon in (2.0, 2.4, 4.8):
+        count = int(np.searchsorted(trajectories.times[1:], horizon + 1e-6, side="right"))
+        if count and horizon <= trajectories.times[-1] + 1e-6:
+            summary[f"mae_through_t{horizon:.1f}"] = mae[:, :count].mean((0, 1)).tolist()
+    if save_path:
+        save_path = Path(save_path)
+        np.savez_compressed(save_path.with_suffix(".npz"), time=trajectories.times[1:],
+                            mae=mae, conservation_relative=cons_relative,
+                            conservation_absolute_sum=cons_abs,
+                            conservation_l1_scaled=cons_scaled,
+                            negative_h_ratio=negatives, min_h=minima,
+                            files=np.array([p.name for p in trajectories.files]))
+        np.savez_compressed(save_path.parent / (save_path.stem + "_examples.npz"),
+                            time=trajectories.times, **examples)
+        save_json(save_path.with_suffix(".json"), summary)
+    return summary
+
+
+def parser(description):
+    arg = argparse.ArgumentParser(description=description)
+    arg.add_argument("--config", type=Path, default=ROOT / "config/config.yaml")
+    return arg

scripts/inference.py

@@ -0,0 +1,51 @@
+"""Reload the selected checkpoint and evaluate every short/long test trajectory."""
+import json
+from pathlib import Path
+
+import torch
+
+from common import (ROOT, Trajectories, build_model, checkpoint_config_matches, device, load_config, output_dir,
+                    parser, provenance, rollout_metrics, save_json, setup, sha256)
+
+
+def main():
+    arg = parser(__doc__)
+    arg.add_argument("--checkpoint", type=Path)
+    args = arg.parse_args()
+    cfg = load_config(args.config)
+    setup(cfg["seed"])
+    target_device = device()
+    out = output_dir(cfg)
+    checkpoint_path = args.checkpoint or ROOT / cfg["paths"]["best_checkpoint"]
+    checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+    status = json.loads((out / "training_status.json").read_text())
+    if status["state"] != "completed" or status["completed_epochs"] != 300:
+        raise RuntimeError("Final inference requires evidence of completed 300-epoch training")
+    if not checkpoint_config_matches(checkpoint["config"], cfg) or checkpoint["epoch"] != status["best_epoch"]:
+        raise ValueError("Checkpoint/config/best-epoch provenance mismatch")
+    model = build_model(cfg, target_device)
+    model.load_state_dict(checkpoint["model_state_dict"], strict=True)
+    if not all(torch.isfinite(p).all() for p in model.parameters()):
+        raise ValueError("Nonfinite checkpoint parameters")
+    information = {"checkpoint": str(checkpoint_path), "sha256": sha256(checkpoint_path),
+                   "selected_epoch": checkpoint["epoch"], "completed_epochs": 300,
+                   "best_val_mse": checkpoint["best_val_loss"],
+                   "parameter_count": sum(p.numel() for p in model.parameters()),
+                   "provenance": provenance()}
+    print("CHECKPOINT_VERIFIED " + json.dumps(information), flush=True)
+    metrics, manifests = {}, {}
+    for split in cfg["evaluation"]["splits"]:
+        data = Trajectories(cfg, split)
+        manifests[split] = data.metadata
+        metrics[split] = rollout_metrics(model, data, target_device,
+                                         batch_size=cfg["evaluation"]["batch_size"],
+                                         save_path=out / split,
+                                         example_cases=cfg["evaluation"]["example_cases"])
+        print("FINAL_METRICS " + json.dumps(metrics[split]), flush=True)
+    save_json(out / "inference_manifest.json", {**information, "data": manifests})
+    save_json(out / "metrics.json", metrics)
+    print("INFERENCE_COMPLETED all=100 trajectories long_steps=120", flush=True)
+
+
+if __name__ == "__main__":
+    main()

scripts/preflight.py

@@ -0,0 +1,80 @@
+"""OneScience installer preflight and independent structural checks; no formal weights."""
+import ast
+import importlib
+import json
+import os
+import shutil
+import time
+
+import torch
+from torch.utils.data import DataLoader
+
+from common import (ROOT, Trajectories, Windows, build_model, device, load_config,
+                    output_dir, parser, provenance, save_json, setup, validate)
+from train import train_batch
+
+
+def main():
+    args = parser(__doc__).parse_args()
+    cfg = load_config(args.config)
+    setup(cfg["seed"])
+    target_device = device()
+    for module in ("onescience", "torch", "numpy", "h5py", "yaml", "matplotlib"):
+        importlib.import_module(module)
+        print("DEPENDENCY_OK " + module, flush=True)
+    for path in ROOT.rglob("*.py"):
+        ast.parse(path.read_text())
+    for command in ("sbatch", "squeue", "sacct"):
+        assert shutil.which(command), command
+    datasets = {split: Trajectories(cfg, split) for split in cfg["data"]["expected_counts"]}
+    out = output_dir(cfg)
+    save_json(out / "dataset_validation.json", {s: d.metadata for s, d in datasets.items()})
+    model = build_model(cfg, target_device)
+    sample = datasets["train"].values[0, :6].unsqueeze(0).to(target_device)
+    model.eval()
+    with torch.no_grad():
+        initial = sample[:, 0]
+        predicted = model(initial)
+        assert predicted.shape == initial.shape
+        assert torch.isfinite(predicted).all()
+        assert predicted[:, 0].min() >= 0
+        drift = (predicted.double().sum((-2, -1)) - initial.double().sum((-2, -1))).abs()
+        scale = initial.double().abs().sum((-2, -1)).clamp_min(1.0)
+        assert (drift / scale).max() < 2e-6
+        # A uniform state remains uniform for all learned transport plans.
+        uniform = torch.ones_like(initial)
+        assert torch.allclose(model(uniform), uniform, atol=2e-6)
+        # Every channel conserves its sum for arbitrary signed momentum.
+        random_state = torch.rand_like(initial)
+        random_state[:, 1:] -= 0.5
+        result = model(random_state)
+        random_drift = (result.double().sum((-2, -1)) - random_state.double().sum((-2, -1))).abs()
+        assert (random_drift / random_state.double().abs().sum((-2, -1)).clamp_min(1)).max() < 2e-6
+    print("STRUCTURE_OK forward, positivity, uniform state, per-channel conservation", flush=True)
+    model.train()
+    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-2)
+    first, terminal = train_batch(model, sample, optimizer, 5)
+    assert any(p.grad is not None for p in model.parameters())
+    validation = validate(model, DataLoader(Windows(datasets["val"], 5), batch_size=2), target_device)
+    assert validation["mse"] >= 0
+    # Independent stop-gradient check on an explicit two-state feedback graph.
+    x = sample[:, 0].detach().requires_grad_(True)
+    intermediate = model(x)
+    final = model(intermediate.detach())
+    assert torch.autograd.grad(final.sum(), x, allow_unused=True)[0] is None
+    props = torch.cuda.get_device_properties(target_device)
+    result = {"status": "passed", "preflight_passed": True, "execution_readiness": "ready",
+              "evidence": {"preflight": {"status": "passed"}}, "installer_reason": "preflight_validation",
+              "execution_channel": "local_slurm", "provenance": provenance(),
+              "gpu_memory_gb": props.total_memory / 2**30,
+              "model_parameters": sum(p.numel() for p in model.parameters()),
+              "structural_checks": ["shape", "finite", "positivity", "uniform_state", "conservation", "detached_feedback"],
+              "real_data_train_batch_loss": {"step1": first, "step5": terminal},
+              "real_data_validation": validation,
+              "formal_training_completed": False, "formal_weights_written": False}
+    save_json(out / "preflight_validation.json", result)
+    print("PREFLIGHT_PASSED " + json.dumps(result), flush=True)
+
+
+if __name__ == "__main__":
+    main()