Physics-Aligned Neural Modeling of Ocean Dynamics

Data-driven models have achieved strong performance in geophysical forecasting, particularly in the atmosphere, but transferring these approaches to the ocean remains challenging due to fundamental differences in geometry and dynamics. The ocean is defined on a discontinuous domain with complex coastlines, strong stratification, and highly anisotropic, surface-driven transport, which are poorly aligned with standard learning architectures. As a result, existing models often exhibit physically inconsistent behavior, including land-sea mixing, unrealistic vertical transport, and incorrect propagation of surface forcing. We propose a physics-aligned neural architecture for ocean forecasting that embeds these structural constraints directly into the model design. The framework operates exclusively on the wet domain, respects boundary-driven forcing, decouples horizontal and vertical transport mechanisms, and incorporates geometry-aware interactions adapted to the structure of the ocean system. The resulting model produces stable long-horizon autoregressive simulations with coherent spatiotemporal structure across physical, sea ice, and biogeochemical variables, while remaining robust under perturbed forcing conditions. Additional analyses further demonstrate stable spectral behavior, physically consistent cross-variable coupling, and improved long-term stability relative to architectures that rely on generic feature-space interactions. These results highlight the importance of incorporating domain-specific geometry and inductive biases directly at the architectural level, rather than relying solely on data scale or loss-based physical constraints.