Update README and model configuration

README.md

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+---
+license: mit
+language:
+- en
+library_name: pytorch
+tags:
+- OneScience
+- fluid-dynamics
+- turbulence
+- fourier-neural-operator
+- physics-informed
+datasets:
+- OneScience-Group/fno
+---
+
+<p align="center">
+  <strong><span style="font-size: 30px;">Spectral-Refiner</span></strong>
+</p>
+
+# Model Introduction
+
+Spectral-Refiner is a physics-residual fine-tuning method for spatiotemporal Fourier neural operators. This project reproduces the two-dimensional forced-turbulence experiment from Table 2 of the paper: an SFNO is first trained on `64 x 64` vorticity trajectories, then its spectral output layer is fine-tuned at `256 x 256` resolution using the \(H^{-1}\) negative Sobolev norm of the Navier–Stokes PDE residual.
+
+Paper: [Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows](https://arxiv.org/abs/2405.17211)
+
+# Model Description
+
+The model takes the first 10 time steps of a two-dimensional vorticity field and predicts the next 40. The base SFNO has four spatiotemporal spectral layers, `12 x 12` spatial modes, 5 temporal modes, and width 20. Spectral-Refiner freezes the base network, expands the output spectral layer to `64 x 64 x 6`, and fine-tunes it for 50 steps with an \(H^{-1}\) PDE-residual objective. This project is an independent OneScience reproduction.
+
+## Intended Uses
+
+| Use case | Description |
+| :--- | :--- |
+| 2D turbulence prediction | Predict future spatiotemporal evolution from historical vorticity fields. |
+| PDE surrogate | Accelerate periodic fluid problems with a Fourier neural operator. |
+| Physics-residual fine-tuning | Constrain the predicted PDE residual with a negative Sobolev norm. |
+
+# 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 for full training and inference.
+- A CPU can run imports and the `--smoke` connectivity test.
+- The reported validation used PyTorch 2.5.1 on one DCU.
+
+### Download the model repository from Hugging Face
+
+```bash
+pip install -U huggingface_hub
+hf download OneScience-Group/Spectral-Refiner --local-dir ./Spectral-Refiner
+cd Spectral-Refiner
+```
+
+### Install the runtime environment
+
+**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
+```
+
+**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/fno \
+  --repo-type dataset \
+  --local-dir ./data
+```
+
+The required files are:
+
+```text
+data/
+├── fnodata_extra_64x64_N1280_v1e-3_T50_steps100_alpha2.5_tau7.pt
+└── fnodata_extra_fp64_256x256_N16_v1e-3_T50_steps100_alpha2.5_tau7.pt
+```
+
+Set `data.train_file` and `data.test_file` in `config/config.yaml` to these files. The default experiment uses 1,152 low-resolution trajectories for training and 128 for validation, mapping 10 input steps to 40 output steps. The high-resolution data is used for evaluation and \(H^{-1}\) spectral fine-tuning on the `256 x 256` grid.
+
+### Train
+
+```bash
+python scripts/train.py --config config/config.yaml
+```
+
+`weight/best_model.pt` stores the base SFNO, Spectral-Refiner output layer, configuration, and weight-selection metric.
+
+### Inference
+
+```bash
+python scripts/inference.py
+```
+
+Predictions are saved to `results/predictions.pt`.
+
+### Evaluation
+
+```bash
+python scripts/result.py
+```
+
+Metrics are printed and saved to `results/metrics.json`.
+
+# 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: [Spectral-Refiner, arXiv:2405.17211](https://arxiv.org/abs/2405.17211).
+- Public implementation: [scaomath/torch-cfd](https://github.com/scaomath/torch-cfd).
+- This repository uses the Hugging Face-compatible MIT identifier (`mit`). Dataset files and other third-party assets retain their original licenses and terms.