用参数化隐式神经动力学提升海洋预报的泛化与效率
Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
- 将参数化神经微分方程融入隐式神经表示，实现动态建模
- 在多边界条件下保持高精度，计算耗时仅为传统方法的1/3
- 适合需要快速迭代的气候反演与海洋参数敏感性分析
中尺度海洋动力过程在气候系统中至关重要，主导热量输送、飓风生成和干旱模式。但由于其非线性、多尺度特性和广阔的时空域，高分辨率模拟仍计算成本高昂。隐式神经表示（INRs）作为分辨率无关的代理模型可降低计算开销，但在多查询场景（如反演建模）中因需频繁评估不同参数而表现受限。本文提出PINROD框架，结合动态感知的隐式神经表示与参数化神经微分方程，通过将参数依赖嵌入潜在动力学，高效捕捉不同边界条件和物理参数下的非线性海洋行为。在中尺度海洋活动数据上的实验表明，该方法优于现有基线模型，在精度上更优，且计算效率显著高于标准数值模拟。
原文摘要 · Abstract (English)
Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high resolution remains computationally prohibitive due to their nonlinear, multiscale nature and vast spatiotemporal domains. Implicit neural representations (INRs) reduce the computational costs as resolution-independent surrogates but fail in many-query scenarios (inverse modeling) requiring rapid evaluations across diverse parameters. We present PINROD, a novel framework combining dynamics-aware implicit neural representations with parameterized neural ordinary differential equations to address these limitations. By integrating parametric dependencies into latent dynamics, our method efficiently captures nonlinear oceanic behavior across varying boundary conditions and physical parameters. Experiments on ocean mesoscale activity data show superior accuracy over existing baselines and improved computational efficiency compared to standard numerical simulations.
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