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

• URL: http://arxiv.org/abs/2503.21588v1
• Date: Thu, 27 Mar 2025 15:04:52 GMT
• Title: Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
• Authors: Guang Zhao, Xihaier Luo, Seungjun Lee, Yihui Ren, Shinjae Yoo, Luke Van Roekel, Balu Nadiga, Sri Hari Krishna Narayanan, Yixuan Sun, Wei Xu,
• Abstract summary: PINROD is a novel framework combining dynamics-aware implicit neural representations with parameterized neural ordinary differential equations.<n>Experiments on ocean mesoscale parametric activity show superior accuracy over existing baselines.
• Score: 15.223198342339803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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