Towards fully differentiable neural ocean model with Veros

Generative AI & LLMs

Published: arXiv: 2511.17427v1

Authors

Etienne Meunier Said Ouala Hugo Frezat Julien Le Sommer Ronan Fablet

Abstract

We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make the model fully compatible with JAX autodifferentiation framework and evaluate the numerical consistency of the resulting implementation. Two illustrative applications are then demonstrated: (i) the correction of an initial ocean state through gradient-based optimization, and (ii) the calibration of unknown physical parameters directly from model observations. These examples highlight how differentiable programming can facilitate end-to-end learning and parameter tuning in ocean modeling. Our implementation is available online.

Paper Summary

Problem

Climate models are crucial for understanding and predicting climate changes. However, tuning these models to accurately reproduce historical data remains a challenging and largely manual process. This can lead to persistent errors and biases in the models, making it difficult to make accurate predictions about the future.

Key Innovation

Researchers have developed a new approach to make climate models more accurate by using a technique called differentiable programming. This involves modifying the ocean model, called VEROS, to make it compatible with a type of computer programming called automatic differentiation. This allows the model to be trained end-to-end, which means that the entire process of calibrating the model can be done automatically, rather than relying on manual adjustments.

Practical Impact

The practical impact of this research is that it can make climate models more accurate and reliable. By using differentiable programming, researchers can train the model to correct its initial state and calibrate its physical parameters automatically. This can lead to better predictions about climate changes and help scientists make more informed decisions about how to mitigate the effects of climate change. Additionally, this approach can also be used for data assimilation, parameter estimation, and physics-informed machine learning in oceanography.

Analogy / Intuitive Explanation

Imagine trying to tune a piano to play a perfect melody. The piano is like the climate model, and the melody is like the accurate reproduction of historical climate data. Traditionally, tuning the piano is a manual process that requires a lot of trial and error. But with differentiable programming, the piano can be designed to tune itself automatically, producing a perfect melody every time. Similarly, the VEROS ocean model can be modified to tune itself automatically, producing more accurate predictions about climate changes.

Paper Information

Categories:

cs.LG

Published Date:

arXiv ID:

2511.17427v1

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