Update dataset card: Add paper, project page, code links, and update citation

README.md

@@ -1,154 +1,156 @@
----
-license: mit
-task_categories:
-- other
-pretty_name: PreGen Navier-Stokes 2D Dataset
-size_categories:
-- 100K<n<1M
-tags:
-- physics
-- fluid-dynamics
-- navier-stokes
-- pde
-- scientific-computing
-- neural-operators
-- foundation-models
-- difficulty-transfer
-- reynolds-number
-- openfoam
----
-
-# PreGen Navier-Stokes 2D Dataset
-
-## Dataset Description
-
-This dataset accompanies the research paper **"Pre-Generating Multi-Difficulty PDE Data For Few-Shot Neural PDE Solvers"** (under review at ICLR 2026). It contains systematically generated 2D incompressible Navier-Stokes fluid flow simulations designed to study **difficulty transfer** in neural PDE solvers.
-
-The key insight: by pre-generating many low and medium difficulty examples and including them with a small number of hard examples, neural PDE solvers can learn high-difficulty physics from far fewer samples.
-
-### Dataset Summary
-
-- **Format:** NumPy arrays (.npy files)
-- **Number of Files:** 9
-- **Simulations per file:** 6,400 trajectories
-- **Timesteps:** 20 per trajectory
-- **Spatial Resolution:** 128 × 128 grid
-- **Solver:** OpenFOAM (icoFoam)
-- **Domain:** 2D Incompressible Navier-Stokes equations
-
-## Difficulty Axes
-
-The dataset systematically varies complexity along three axes:
-
-### 1. **Geometry Axis** (Number of Obstacles)
-Simulations in flow-past-object (FPO) configuration with varying obstacle complexity:
-
-- **Easy:** No obstacles (open channel flow)
-- **Medium:** Single square obstacle
-- **Hard:** 2-10 randomly placed square obstacles
-
-**Files:**
-- `Geometry_Axis/FPO_Geometry_Easy_NoObstacle.npy` (47 GB)
-- `Geometry_Axis/FPO_Geometry_Medium_SingleObstacle.npy` (47 GB)
-- `Geometry_Axis/FPO_Geometry_Hard_MultiObstacle.npy` (47 GB)
-
-### 2. **Physics Axis** (Reynolds Number)
-Simulations with varying flow complexity via Reynolds number:
-
-**Multi-Obstacle Flows:**
-- **Easy:** Re ∈ [100, 1000] - laminar regime
-- **Medium:** Re ∈ [2000, 4000] - transitional regime
-- **Hard:** Re ∈ [8000, 10000] - turbulent regime
-
-**Files:**
-- `Physics_Axis/MultiObstacle/FPO_Physics_MultiObstacle_Easy_Re100-1000.npy` (47 GB)
-- `Physics_Axis/MultiObstacle/FPO_Physics_MultiObstacle_Medium_Re2000-4000.npy` (47 GB)
-- `Physics_Axis/MultiObstacle/FPO_Physics_MultiObstacle_Hard_Re8000-10000.npy` (47 GB)
-
-**No-Obstacle Flows:**
-- `Physics_Axis/NoObstacle/FPO_Physics_NoObstacle_Easy_Re100-1000.npy` (47 GB)
-
-### 3. **Combined Axis** (Geometry + Physics)
-Combined variations in both geometry and Reynolds number:
-
-- **Easy:** No obstacles + low Re ([100, 1000])
-- **Medium:** Single obstacle + medium Re ([2000, 4000])
-- **Hard:** Multiple obstacles + high Re ([8000, 10000])
-
-**File:**
-- `Combined_Axis/FPO_Combined_Medium_SingleObstacle_MedRe.npy` (47 GB)
-
-### 4. **Special Configuration**
-- `Special/FPO_Cylinder_Hole_Location_6284.npy` (47 GB) - Cylinder with hole at specific location
-
-## Data Format
-
-Each `.npy` file contains a NumPy array with shape: `(6400, 20, 128, 128, 6)`
-
-**Dimensions:**
-- **6400**: Number of simulation trajectories
-- **20**: Timesteps per trajectory
-- **128 × 128**: Spatial grid resolution
-- **6**: Channels (features)
-
-**Channels (in order):**
-1. **u** - Horizontal velocity component (m/s)
-2. **v** - Vertical velocity component (m/s)
-3. **p** - Kinematic pressure (m²/s²)
-4. **Re_normalized** - Normalized Reynolds number
-5. **Binary mask** - Geometry encoding (1 = obstacle, 0 = fluid)
-6. **SDF** - Signed distance field to nearest obstacle boundary
-
-## Usage
-
-```python
-import numpy as np
-from huggingface_hub import hf_hub_download
-
-# Download a specific difficulty level
-file_path = hf_hub_download(
-    repo_id="sage-lab/PreGen-NavierStokes-2D",
-    filename="Geometry_Axis/FPO_Geometry_Easy_NoObstacle.npy",
-    repo_type="dataset"
-)
-
-# Load the data
-data = np.load(file_path)
-print(f"Data shape: {data.shape}")  # (6400, 20, 128, 128, 6)
-
-# Extract individual trajectories
-trajectory_0 = data[0]  # Shape: (20, 128, 128, 6)
-
-# Extract velocity and pressure
-u = trajectory_0[:, :, :, 0]  # Horizontal velocity
-v = trajectory_0[:, :, :, 1]  # Vertical velocity
-p = trajectory_0[:, :, :, 2]  # Pressure
-mask = trajectory_0[:, :, :, 4]  # Binary geometry mask
-sdf = trajectory_0[:, :, :, 5]  # Signed distance field
-```
-
-## Citation
-
-If you use this dataset, please cite:
-
-```bibtex
-@inproceedings{pregen2026,
-  title={Pre-Generating Multi-Difficulty {PDE} Data For Few-Shot Neural {PDE} Solvers},
-  author={Anonymous},
-  booktitle={Under review at International Conference on Learning Representations (ICLR)},
-  year={2026},
-  url={https://openreview.net}
-}
-```
-
-**Note:** Citation will be updated once the paper is published.
-
-## License
-
-MIT License
-
----
-
-**Dataset Version:** 1.0
-**Last Updated:** 2024
-**Status:** Research dataset under peer review
+---
+license: mit
+size_categories:
+- 100K<n<1M
+task_categories:
+- other
+pretty_name: PreGen Navier-Stokes 2D Dataset
+tags:
+- physics
+- fluid-dynamics
+- navier-stokes
+- pde
+- scientific-computing
+- neural-operators
+- foundation-models
+- difficulty-transfer
+- reynolds-number
+- openfoam
+language:
+- en
+---
+
+# PreGen Navier-Stokes 2D Dataset
+
+[Paper](https://huggingface.co/papers/2512.00564) | [Project Page](https://naman-choudhary-ai-ml.github.io/pde-difficulty-transfer/) | [Code](https://github.com/Naman-Choudhary-AI-ML/pregenerating-pde)
+
+## Dataset Description
+
+This dataset accompanies the research paper **[Pre-Generating Multi-Difficulty PDE Data For Few-Shot Neural PDE Solvers](https://huggingface.co/papers/2512.00564)** (under review at ICLR 2026). It contains systematically generated 2D incompressible Navier-Stokes fluid flow simulations designed to study **difficulty transfer** in neural PDE solvers.
+
+The key insight: by pre-generating many low and medium difficulty examples and including them with a small number of hard examples, neural PDE solvers can learn high-difficulty physics from far fewer samples.
+
+### Dataset Summary
+
+- **Format:** NumPy arrays (.npy files)
+- **Number of Files:** 9
+- **Simulations per file:** 6,400 trajectories
+- **Timesteps:** 20 per trajectory
+- **Spatial Resolution:** 128 × 128 grid
+- **Solver:** OpenFOAM (icoFoam)
+- **Domain:** 2D Incompressible Navier-Stokes equations
+
+## Difficulty Axes
+
+The dataset systematically varies complexity along three axes:
+
+### 1. **Geometry Axis** (Number of Obstacles)
+Simulations in flow-past-object (FPO) configuration with varying obstacle complexity:
+
+- **Easy:** No obstacles (open channel flow)
+- **Medium:** Single square obstacle
+- **Hard:** 2-10 randomly placed square obstacles
+
+**Files:**
+- `Geometry_Axis/FPO_Geometry_Easy_NoObstacle.npy` (47 GB)
+- `Geometry_Axis/FPO_Geometry_Medium_SingleObstacle.npy` (47 GB)
+- `Geometry_Axis/FPO_Geometry_Hard_MultiObstacle.npy` (47 GB)
+
+### 2. **Physics Axis** (Reynolds Number)
+Simulations with varying flow complexity via Reynolds number:
+
+**Multi-Obstacle Flows:**
+- **Easy:** Re ∈ [100, 1000] - laminar regime
+- **Medium:** Re ∈ [2000, 4000] - transitional regime
+- **Hard:** Re ∈ [8000, 10000] - turbulent regime
+
+**Files:**
+- `Physics_Axis/MultiObstacle/FPO_Physics_MultiObstacle_Easy_Re100-1000.npy` (47 GB)
+- `Physics_Axis/MultiObstacle/FPO_Physics_MultiObstacle_Medium_Re2000-4000.npy` (47 GB)
+- `Physics_Axis/MultiObstacle/FPO_Physics_MultiObstacle_Hard_Re8000-10000.npy` (47 GB)
+
+**No-Obstacle Flows:**
+- `Physics_Axis/NoObstacle/FPO_Physics_NoObstacle_Easy_Re100-1000.npy` (47 GB)
+
+### 3. **Combined Axis** (Geometry + Physics)
+Combined variations in both geometry and Reynolds number:
+
+- **Easy:** No obstacles + low Re ([100, 1000])
+- **Medium:** Single obstacle + medium Re ([2000, 4000])
+- **Hard:** Multiple obstacles + high Re ([8000, 10000])
+
+**File:**
+- `Combined_Axis/FPO_Combined_Medium_SingleObstacle_MedRe.npy` (47 GB)
+
+### 4. **Special Configuration**
+- `Special/FPO_Cylinder_Hole_Location_6284.npy` (47 GB) - Cylinder with hole at specific location
+
+## Data Format
+
+Each `.npy` file contains a NumPy array with shape: `(6400, 20, 128, 128, 6)`
+
+**Dimensions:**
+- **6400**: Number of simulation trajectories
+- **20**: Timesteps per trajectory
+- **128 × 128**: Spatial grid resolution
+- **6**: Channels (features)
+
+**Channels (in order):**
+1. **u** - Horizontal velocity component (m/s)
+2. **v** - Vertical velocity component (m/s)
+3. **p** - Kinematic pressure (m²/s²)
+4. **Re_normalized** - Normalized Reynolds number
+5. **Binary mask** - Geometry encoding (1 = obstacle, 0 = fluid)
+6. **SDF** - Signed distance field to nearest obstacle boundary
+
+## Usage
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download a specific difficulty level
+file_path = hf_hub_download(
+    repo_id="sage-lab/PreGen-NavierStokes-2D",
+    filename="Geometry_Axis/FPO_Geometry_Easy_NoObstacle.npy",
+    repo_type="dataset"
+)
+
+# Load the data
+data = np.load(file_path)
+print(f"Data shape: {data.shape}")  # (6400, 20, 128, 128, 6)
+
+# Extract individual trajectories
+trajectory_0 = data[0]  # Shape: (20, 128, 128, 6)
+
+# Extract velocity and pressure
+u = trajectory_0[:, :, :, 0]  # Horizontal velocity
+v = trajectory_0[:, :, :, 1]  # Vertical velocity
+p = trajectory_0[:, :, :, 2]  # Pressure
+mask = trajectory_0[:, :, :, 4]  # Binary geometry mask
+sdf = trajectory_0[:, :, :, 5]  # Signed distance field
+```
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{pregen2025,
+  title={Pre-Generating Multi-Difficulty PDE Data For Few-Shot Neural PDE Solvers},
+  author={Choudhary, Naman and Singh, Vedant and Talwalkar, Ameet and Boffi, Nicholas Matthew and Khodak, Mikhail and Marwah, Tanya},
+  journal={arXiv preprint},
+  year={2025},
+  url={https://huggingface.co/papers/2512.00564}
+}
+```
+
+## License
+
+MIT License
+
+---
+
+**Dataset Version:** 1.0
+**Last Updated:** 2024
+**Status:** Research dataset under peer review