Update README and model configuration

README.md

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+---
+license: mit
+language:
+- en
+library_name: pytorch
+tags:
+- OneScience
+- fluid-dynamics
+- neural-operator
+- radon-transform
+- darcy-flow
+datasets:
+- OneScience-Group/cfd_benchmark
+---
+
+<p align="center">
+  <strong><span style="font-size: 30px;">RNO</span></strong>
+</p>
+
+# Model Introduction
+
+RNO (Radon Neural Operator), proposed by researchers from Zhejiang University of Technology, learns PDE solution mappings with both global and local features in the sinogram domain through the Radon transform.
+
+This repository is an independent OneScience reproduction of the Darcy-flow experiment. RNO learns the parameterized Darcy solution operator and predicts steady pressure from a porous-medium permeability or diffusion-coefficient field.
+
+Paper: [Solving Partial Differential Equations via Radon Neural Operator](https://proceedings.neurips.cc/paper_files/paper/2025/file/e66233a208ef32f56df6312263239fa0-Paper-Conference.pdf)
+
+# Model Description
+
+For Darcy flow, RNO maps a two-dimensional permeability or diffusion-coefficient field \(a(x,y)\) to its scalar pressure solution \(u(x,y)\).
+
+The architecture consists of feature lifting, Physics-Attention, a Radon block, and output projection. The input field and spatial coordinates are lifted to high-dimensional features; Physics-Attention extracts nonlocal information; and the Radon block projects features into the sinogram domain. Angle reweighting and sinogram convolution learn the contribution of different projection directions before filtered back-projection restores spatial features for the final Darcy solution.
+
+## Intended Uses
+
+| Use case | Description |
+| --- | --- |
+| Darcy-flow prediction | Predict steady porous-medium pressure from a two-dimensional permeability or diffusion field. |
+| Parameterized PDE solution | Learn an operator from coefficients, initial conditions, or boundary conditions to PDE solutions. |
+| Scientific surrogate | Replace part of a costly numerical solve with fast batched prediction. |
+| Cross-resolution prediction | Evaluate the learned operator at different spatial resolutions when supported by the training setup. |
+
+# Usage
+
+## 1. OneCode
+
+[Launch the OneCode AI-for-Science environment](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
+
+## 2. Manual Setup
+
+**Hardware requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can run imports and small connectivity checks, but full training and inference will be slow.
+- DCU users should install DTK 25.04.2 or later, or the OneScience-recommended version for the cluster.
+
+### Download the model repository from Hugging Face
+
+```bash
+pip install -U huggingface_hub
+hf download OneScience-Group/RNO --local-dir ./RNO
+cd RNO
+```
+
+### Install the runtime environment
+
+**DCU environment**
+
+```bash
+# Activate DTK first.
+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
+```
+
+**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
+```
+
+### Download the training dataset from Hugging Face
+
+```bash
+hf download OneScience-Group/cfd_benchmark \
+  --repo-type dataset \
+  --local-dir ./data
+```
+
+The Darcy files are stored under `data/data/darcy/` after this download:
+
+```text
+data/data/darcy/
+├── piececonst_r421_N1024_smooth1.mat
+└── piececonst_r421_N1024_smooth2.mat
+```
+
+Set `data.root` in `config/config.yaml` to the Darcy directory. Each MAT file contains 1,024 regular-grid samples at the original `421 x 421` resolution:
+
+- `coeff`: the Darcy permeability or diffusion-coefficient field.
+- `sol`: the corresponding steady pressure solution.
+
+This experiment uses `piececonst_r421_N1024_smooth1.mat` for training and `piececonst_r421_N1024_smooth2.mat` for testing, with downsampling and normalization defined by the experiment configuration.
+
+### Train
+
+```bash
+python scripts/train.py --config config/config.yaml
+```
+
+Per-epoch metrics are logged and printed at the configured interval. The checkpoint with the lowest training relative L2 is saved to `weight/best_model.pth`.
+
+### Pretrained weights
+
+The repository includes a Darcy-trained RNO checkpoint at `weight/best_model.pth` for inference or continued training.
+
+### Inference and evaluation
+
+```bash
+python scripts/inference.py \
+  --config config/config.yaml \
+  --checkpoint weight/best_model.pth \
+  --device auto
+```
+
+The script evaluates the fixed test set, prints the mean per-sample relative L2, computes relative L2 and gradient-relative L2 against paper references, and writes:
+
+```text
+results/evaluation_metrics.json
+results/predictions.npz
+```
+
+`predictions.npz` contains predicted and target pressure fields, normalized permeability fields, and per-sample relative L2 values.
+
+### Visualization
+
+```bash
+python scripts/result.py --results results
+```
+
+Outputs:
+
+```text
+results/training_curve.png
+results/darcy_prediction.png
+results/visualization_summary.json
+```
+
+- `training_curve.png` shows total training loss, relative L2, and gradient-relative L2.
+- `darcy_prediction.png` compares permeability, target pressure, predicted pressure, and absolute error.
+- `visualization_summary.json` records visualization paths, test relative L2, and the paper comparison.
+
+# OneScience
+
+| Platform | OneScience repository | OneSkills 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: [Solving Partial Differential Equations via Radon Neural Operator](https://proceedings.neurips.cc/paper_files/paper/2025/file/e66233a208ef32f56df6312263239fa0-Paper-Conference.pdf).
+- Official implementation: [wenbin-lu/Radon-Neural-Operator](https://github.com/wenbin-lu/Radon-Neural-Operator), released under the [MIT License](https://github.com/wenbin-lu/Radon-Neural-Operator/blob/main/LICENSE).
+- This repository uses the Hugging Face-compatible MIT identifier (`mit`). The dataset and other third-party resources remain subject to their original licenses and terms.