Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting

2025-03-27

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.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

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

• 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

• Combines implicit neural representations with parameterized ODEs
• Integrates parametric dependencies into latent dynamics
• Improves accuracy and computational efficiency in simulations