Understand
Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing.
- Traditional autoregressive machine learning models often fail in these tasks as minor errors accumulate and lead to rapid forecast degradation.
- To address this problem, we propose NeuralOM, a general neural operator framework designed for simulating complex, slow-changing dynamics.
- NeuralOM's core consists of two key innovations: (1) a Progressive Residual Correction Framework that decomposes the forecasting task into a series of fine-grained refinement steps, effectively suppressing long-term error accumulation; and (2) a Physics-Guided Graph Network whose built-in adaptive messaging mechanism explicitly models multi-scale physical interactions, such as gradient-driven flows and multiplicative couplings, thereby enhancing physical consistency while maintaining computational efficiency.