NeuralOGCM: Differentiable Ocean Modeling with Learnable Physics
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912759896080384 |
|---|---|
| author | Wu, Hao Gao, Yuan Xu, Fan Zhang, Fan Liu, Guangliang Liang, Yuxuan Huang, Xiaomeng |
| author_facet | Wu, Hao Gao, Yuan Xu, Fan Zhang, Fan Liu, Guangliang Liang, Yuxuan Huang, Xiaomeng |
| contents | High-precision scientific simulation faces a long-standing trade-off between computational efficiency and physical fidelity. To address this challenge, we propose NeuralOGCM, an ocean modeling framework that fuses differentiable programming with deep learning. At the core of NeuralOGCM is a fully differentiable dynamical solver, which leverages physics knowledge as its core inductive bias. The learnable physics integration captures large-scale, deterministic physical evolution, and transforms key physical parameters (e.g., diffusion coefficients) into learnable parameters, enabling the model to autonomously optimize its physical core via end-to-end training. Concurrently, a deep neural network learns to correct for subgrid-scale processes and discretization errors not captured by the physics model. Both components work in synergy, with their outputs integrated by a unified ODE solver. Experiments demonstrate that NeuralOGCM maintains long-term stability and physical consistency, significantly outperforming traditional numerical models in speed and pure AI baselines in accuracy. Our work paves a new path for building fast, stable, and physically-plausible models for scientific computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuralOGCM: Differentiable Ocean Modeling with Learnable Physics Wu, Hao Gao, Yuan Xu, Fan Zhang, Fan Liu, Guangliang Liang, Yuxuan Huang, Xiaomeng Machine Learning Artificial Intelligence High-precision scientific simulation faces a long-standing trade-off between computational efficiency and physical fidelity. To address this challenge, we propose NeuralOGCM, an ocean modeling framework that fuses differentiable programming with deep learning. At the core of NeuralOGCM is a fully differentiable dynamical solver, which leverages physics knowledge as its core inductive bias. The learnable physics integration captures large-scale, deterministic physical evolution, and transforms key physical parameters (e.g., diffusion coefficients) into learnable parameters, enabling the model to autonomously optimize its physical core via end-to-end training. Concurrently, a deep neural network learns to correct for subgrid-scale processes and discretization errors not captured by the physics model. Both components work in synergy, with their outputs integrated by a unified ODE solver. Experiments demonstrate that NeuralOGCM maintains long-term stability and physical consistency, significantly outperforming traditional numerical models in speed and pure AI baselines in accuracy. Our work paves a new path for building fast, stable, and physically-plausible models for scientific computing. |
| title | NeuralOGCM: Differentiable Ocean Modeling with Learnable Physics |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.11525 |