--- license: mit datasets: - Kratos-AI/physics-problems pipeline_tag: time-series-forecasting tags: - climate --- #

NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation

[![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)
--- >**NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation**
[Yuan Gao](https://scholar.google.com.hk/citations?hl=zh-CN&user=4JpRnU4AAAAJ&view_op=list_works&sortby=pubdate)† , [Ruiqi Shu](https://scholar.google.com.hk/citations?user=WKBB3r0AAAAJ&hl=zh-CN&oi=sra)† , [Hao Wu](https://easylearningscores.github.io/)† ,[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), [Kun Wang](https://scholar.google.com.hk/citations?user=UnyqjWQAAAAJ&hl=zh-CN), [Xiaomeng Huang](http://faculty.dess.tsinghua.edu.cn/huangxiaomeng/en/index.htm)*
(† Equal contribution, * Corresponding Author)
> **Abstract:** *Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing. Traditional autoregressive machine learning models often fail in these tasks as minor errors accumulate and lead to rapid forecast degradation. To address this problem, we propose NeuralOM, a general neural operator framework designed for simulating complex, slow-changing dynamics. NeuralOM's core consists of two key innovations: (1) a Progressive Residual Correction Framework that decomposes the forecasting task into a series of fine-grained refinement steps, effectively suppressing long-term error accumulation; and (2) a Physics-Guided Graph Network whose built-in adaptive messaging mechanism explicitly models multi-scale physical interactions, such as gradient-driven flows and multiplicative couplings, thereby enhancing physical consistency while maintaining computational efficiency. We validate NeuralOM on the challenging task of global Subseasonal-to-Seasonal (S2S) ocean simulation. Extensive experiments demonstrate that NeuralOM not only surpasses state-of-the-art models in forecast accuracy and long-term stability, but also excels in simulating extreme events. For instance, at a 60-day lead time, NeuralOM achieves a 13.3% lower RMSE compared to the best-performing baseline, offering a stable, efficient, and physically-aware paradigm for data-driven scientific computing. Codes link: https://github.com/YuanGao-YG/NeuralOM.* --- ## News 🚀 * **2025.07.28**: Inference codes for global ocean forecasting are released. * **2025.06.01**: Inference codes for global ocean simulation are released. * **2025.05.27**: Paper is released on [ArXiv](https://arxiv.org/abs/2505.21020). ## Notes The intact project is avilable 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 ``` # 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 for Global Ocean Simulation 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: ``` sh inference_simulation.sh ``` ### Inference for Global Ocean Forecasting Preparing the test data as follows: ``` ./data/ |--test | |--2020.h5 |--test_atmos | |--2020.h5 |--mean_s_t_ssh.npy |--std_s_t_ssh.npy |--mean_atmos.npy |--std_atmos.npy |--climate_mean_s_t_ssh.npy |--land_mask.h5 ``` Run the following script: ``` sh inference_forecasting.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
### Global Ocean Forecasting and Extreme Event Assessment
## Citation ``` @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 Wang, Kun 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).