让海洋模型可微分，实现自动优化与参数校准。
Towards fully differentiable neural ocean model with Veros
- 改造VEROS模型使其兼容JAX自动微分框架
- 实现初始状态优化与物理参数从观测数据直接校准
- 适合需要端到端学习的海洋建模研究者
我们提出了VEROS海洋模型的可微分扩展，使其中的动态核心支持自动微分。描述了使模型完全兼容JAX自动微分框架所需的关键修改，并评估了实现后的数值一致性。随后展示了两个应用实例：(i) 通过梯度优化修正初始海洋状态；(ii) 直接从模型观测数据校准未知物理参数。这些例子表明，可微编程能促进海洋建模中的端到端学习与参数调优。我们的实现已公开可用。
原文摘要 · Abstract (English)
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.
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