Add task category, fix typos, and improve documentation

README.md

@@ -1,39 +1,62 @@
 ---
-license: mit
 language:
 - en
-tags:
-- physcis
-- physcis-informed
+license: mit
 pretty_name: Neural Parametric Solver
+task_categories:
+- other
+tags:
+- physics
+- physics-informed
 ---
 
-## Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods 
+# Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods 
+
+This repository provides the datasets used in the paper "[Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods](https://huggingface.co/papers/2410.06820)", presented at ICLR 2025. 
 
-This space provides the datasets used in the paper "Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods". 
+[Project Page](https://2ailesb.github.io/paperpages/neural-solver.html) | [ArXiv](https://arxiv.org/abs/2410.06820) | [Code](https://github.com/2ailesB/neural-parametric-solver)
 
-- Project Page: https://2ailesb.github.io/paperpages/neural-solver.html
-- ArXiV: https://arxiv.org/abs/2410.06820
-- Code: https://github.com/2ailesB/neural-parametric-solver
+### Usage
+
+To use these datasets with the provided code, follow the setup instructions from the [official repository](https://github.com/2ailesB/neural-parametric-solver):
+
+```bash
+# Setup
+conda create -n neural-parametric-solver python=3.10.11
+pip install -e .
+
+# Example: Train a neural solver on the Helmholtz dataset
+python3 main.py dataset=helmholtz exp.lr=0.01 model.input_bc=1 model.input_gradtheta=1
+```
 
 ### PDEs
 We provide 9 datasets:
-- Helmholtz equation 1d: 4 versions for this PDE with varying difficulties depending on the range of the parameter $\omega$.
+- **Helmholtz equation 1d**: 4 versions for this PDE with varying difficulties depending on the range of the parameter $\omega$.
   - (0.5, 3): toy
   - (0.5, 10): medium
   - (0.5, 50): hard
   - (-5, 55): used for OOD experiments
-- Poisson equation 1d: 2 versions of the Poisosn equation:
+- **Poisson equation 1d**: 2 versions of the Poisson equation:
   - Scalar forcing term
   - Multiscale functional forcing term
-- Non-Linear Reaction Diffusion PDE 1d (temporal)
-- Advection PDE 1d (temporal): extracted from PDEBench datasets
-- Heat 2d (temporal)
+- **Non-Linear Reaction Diffusion PDE 1d (temporal)**
+- **Advection PDE 1d (temporal)**: extracted from PDEBench datasets
+- **Heat 2d (temporal)**
 
-Please refer to our [paper](https://arxiv.org/abs/2410.06820) or [code](https://github.com/2ailesB/neural-parametric-solver) for additional details on the PDE, parameters range or Datasets and Dataloaders. 
+Please refer to the [paper](https://arxiv.org/abs/2410.06820) or [code](https://github.com/2ailesB/neural-parametric-solver) for additional details on the PDEs, parameter ranges, and Dataloaders. 
 
 ### What's inside the datasets
 
-Each dataset provide the PDE trajectory $u$ along with the PDE parameters, forcings terms (if involved), initial conditions (if involved), boundary conditions (if involved). 
-The [torch Datasets](https://github.com/2ailesB/neural-parametric-solver/tree/main/ngd_datasets) associated class return the data under a list containing: (params, forcings, ic, bc), position x, solution u, index of the trajectory. 
+Each dataset provides the PDE trajectory $u$ along with the PDE parameters, forcing terms (if involved), initial conditions (if involved), and boundary conditions (if involved). 
+
+The [torch Datasets](https://github.com/2ailesB/neural-parametric-solver/tree/main/ngd_datasets) associated class returns the data as a list containing: `(params, forcings, ic, bc)`, position `x`, solution `u`, and the index of the trajectory. 
 
+### Citation
+```bibtex
+@inproceedings{leboudec2024learning,
+  title={Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods},
+  author={Le Boudec, Lise and de Bezenac, Emmanuel and Serrano, Louis and Regueiro-Espino, Ramon Daniel and Yin, Yuan and Gallinari, Patrick},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025}
+}
+```