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Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
🤖 AI Summary
High-resolution baroclinic ocean mesoscale process simulation incurs prohibitive computational cost, while existing implicit neural representations (INRs) suffer from poor generalizability and inadequate support for rapid multi-parameter queries (e.g., inverse modeling). Method: We propose a physics-driven, dynamically generalizable modeling framework that—novelty—embeds parameterized neural ODEs into the latent dynamics of INRs, enabling continuous spatiotemporal modeling jointly driven by boundary conditions and physical parameters. Our approach integrates INRs, parameterized neural ODEs, dynamics-aware latent space modeling, and multi-scale ocean data-driven training. Contribution/Results: Evaluated on real mesoscale ocean data, our method achieves significantly higher accuracy than state-of-the-art INRs and simplified models, accelerates inference by 1–2 orders of magnitude, and drastically reduces computational overhead compared to traditional numerical simulations—establishing a new paradigm for rapid forecasting and inversion of ocean processes across diverse scenarios and parameter configurations in climate systems.
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📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Simulating high-resolution mesoscale ocean dynamics is computationally prohibitive
Implicit neural representations fail in many-query scenarios requiring rapid evaluations
Existing methods lack efficiency in capturing nonlinear oceanic behavior across parameters
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combines implicit neural representations with parameterized ODEs
Integrates parametric dependencies into latent dynamics
Improves accuracy and computational efficiency in simulations
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