Add comprehensive model card for NeuralOM

This PR adds a comprehensive model card for the NeuralOM model, which was introduced in the paper [NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation](https://huggingface.co/papers/2505.21020).

It includes:
- The `pipeline_tag: time-series-forecasting`, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=time-series-forecasting.
- A link to the paper and the GitHub repository.
- The abstract, key architectural diagrams, performance metrics, installation/inference instructions, and citation information from the original GitHub README.

This greatly enhances the discoverability and usability of the model on the Hugging Face Hub.

README.md

@@ -1,3 +1,156 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: time-series-forecasting
+---
+
+# NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation
+
+This repository contains the NeuralOM model, presented in the paper [NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation](https://huggingface.co/papers/2505.21020).
+
+<div align="center">
+
+[![ArXiv](https://img.shields.io/badge/NeuralOM-ArXiv-red.svg)](https://arxiv.org/abs/2505.21020)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue)](https://huggingface.co/YuanGao-YG/NeuralOM/tree/main)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-black.svg?logo=github)](https://github.com/YuanGao-YG/NeuralOM)
+
+</div>
+<div align=center>
+<img src="https://huggingface.co/YuanGao-YG/NeuralOM/resolve/main/img/fig_NeuralOM.jpg" width="1080">
+</div>
+
+---
+>**NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation**<br>  [Yuan Gao](https://scholar.google.com.hk/citations?hl=zh-CN&user=4JpRnU4AAAAJ&view_op=list_works&sortby=pubdate)<sup>† </sup>, [Ruiqi Shu](https://scholar.google.com.hk/citations?user=WKBB3r0AAAAJ&hl=zh-CN&oi=sra)<sup>† </sup>, [Hao Wu](https://easylearningscores.github.io/)<sup>† </sup>,[Fan Xu](https://scholar.google.com.hk/citations?hl=zh-CN&user=qfMSkBgAAAAJ&view_op=list_works&sortby=pubdate), [Yanfei Xiang](https://orcid.org/0000-0002-5755-4114), [Ruijian Gou](https://scholar.google.com.hk/citations?user=YU7AZzQAAAAJ&hl=zh-CN), [Qingsong Wen](https://sites.google.com/site/qingsongwen8/), [Xian Wu](https://scholar.google.com.hk/citations?hl=zh-CN&user=lslB5jkAAAAJ&view_op=list_works&sortby=pubdate), [Xiaomeng Huang](http://faculty.dess.tsinghua.edu.cn/huangxiaomeng/en/index.htm)<sup>* </sup> <br>
+(† Equal contribution, * Corresponding Author)<br>
+
+
+> **Abstract:** *Accurate Subseasonal-to-Seasonal (S2S) ocean simulation is critically important for marine research, yet remains challenging due to its substantial thermal inertia and extended time delay. Machine learning (ML)-based models have demonstrated significant advancements in simulation accuracy and computational efficiency compared to traditional numerical methods. Nevertheless, a significant limitation of current ML models for S2S ocean simulation is their inadequate incorporation of physical consistency and the slow-changing properties of the ocean system. In this work, we propose a neural ocean model (NeuralOM) for S2S ocean simulation with a multi-scale interactive graph neural network to emulate diverse physical phenomena associated with ocean systems effectively. Specifically, we propose a multi-stage framework tailored to model the ocean's slowly changing nature. Additionally, we introduce a multi-scale interactive messaging module to capture complex dynamical behaviors, such as gradient changes and multiplicative coupling relationships inherent in ocean dynamics. Extensive experimental evaluations confirm that our proposed NeuralOM outperforms state-of-the-art models in S2S and extreme event simulation. The codes are available at [https://github.com/YuanGao-YG/NeuralOM](https://github.com/YuanGao-YG/NeuralOM).*\
+---
+
+## News 🚀
+*   **2025.06.01**: Codes for inference are released.
+*   **2025.05.27**: Paper is released on [ArXiv](https://arxiv.org/abs/2505.21020).
+
+## Notes
+
+The intact project is available on [Hugging Face](https://huggingface.co/YuanGao-YG/NeuralOM/tree/main), you can find the pretrained models, test data on Hugging Face and put them in the same location.
+
+## Quick Start
+
+### Installation
+
+-   cuda 11.8
+
+```bash
+# git clone this repository
+git clone https://github.com/YuanGao-YG/NeuralOM.git
+cd NeuralOM
+
+# create new anaconda env
+conda env create -f environment.yml
+conda activate neuralom
+```
+
+
+### Inference
+
+Preparing the test data as follows:
+
+```
+./data/
+|--test
+|  |--2020.h5
+|--mean_s_t_ssh.npy
+|--std_s_t_ssh.npy
+|--climate_mean_s_t_ssh.npy
+|--land_mask.h5
+```
+
+Run the following script:
+
+```bash
+sh inference.sh
+```
+   
+## Training
+
+The training codes will be released after the paper is accepted.
+
+**1. Prepare Data**
+
+Preparing the train, valid, and test data as follows:
+
+```
+./data/
+|--train
+|  |--1993.h5
+|  |--1994.h5
+|  |--......
+|  |--2016.h5
+|  |--2017.h5
+|--valid
+|  |--2018.h5
+|  |--2019.h5
+|--test
+|  |--2020.h5
+|--mean_s_t_ssh.npy
+|--std_s_t_ssh.npy
+|--climate_mean_s_t_ssh.npy
+|--land_mask.h5
+```
+
+For data ranging from 1993 to 2020, each h5 file includes a key named 'fields' with the shape [T, C, H, W] (T=365/366, C=97, H=361, W=720)
+
+
+**2. Model Training**
+
+
+-   **Single GPU Training**
+  
+    Continue update
+-   **Single-node Multi-GPU Training**
+
+    Continue update
+-   **Multi-node Multi-GPU Training**
+
+    Continue update
+
+
+
+## Performance
+### Global Ocean Simulation
+
+</div>
+<div align=center>
+<img src="https://huggingface.co/YuanGao-YG/NeuralOM/resolve/main/img/tab_acc_rmse.jpg" width="1080">
+</div>
+
+</div>
+<div align=center>
+<img src="https://huggingface.co/YuanGao-YG/NeuralOM/resolve/main/img/fig_rmse_acc.jpg" width="1080">
+</div>
+
+
+</div>
+<div align=center>
+<img src="https://huggingface.co/YuanGao-YG/NeuralOM/resolve/main/img/fig_visual.jpg" width="1080">
+</div>
+
+### Extreme Event Assessment
+
+</div>
+<div align=center>
+<img src="https://huggingface.co/YuanGao-YG/NeuralOM/resolve/main/img/fig_csi.jpg" width="1080">
+</div>
+
+## Citation
+
+```bibtex
+@article{gao2025neuralom,
+  title={NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation},
+  author={Gao, Yuan and Shu, Ruiqi and Wu, Hao and Xu, Fan and Xiang, Yanfei and Gou, Ruijian and Wen, Qingsong and Wu, Xian and Huang, Xiaomeng},
+  journal={arXiv preprint arXiv:2505.21020},
+  year={2025}
+}
+```
+
+#### If you have any questions, please contact [yuangao24@mails.tsinghua.edu.cn](mailto:yuangao24@mails.tsinghua.edu.cn), [srq24@mails.tsinghua.edu.cn](mailto:srq24@mails.tsinghua.edu.cn), [wuhao2022@mail.ustc.edu.cn](mailto:wuhao2022@mail.ustc.edu.cn).