Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance

TL;DR

This paper introduces ST-PAD, a two-stage physics-aware framework for high-precision spatio-temporal fluid dynamics modeling, combining parameter diffusion guidance and physical constraints to improve prediction accuracy and robustness.

Contribution

The paper presents a novel two-stage framework that integrates physical-awareness and parameter diffusion for improved fluid dynamics modeling and out-of-distribution generalization.

Findings

Outperforms existing models on benchmark datasets

Effectively captures local representations of fluid dynamics

Enhances robustness in out-of-distribution scenarios

Abstract

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance. In the upstream stage, we design a vector quantization reconstruction module with temporal evolution characteristics, ensuring balanced and resilient parameter distribution by introducing general physical constraints. In the downstream stage, a diffusion probability network involving parameters is utilized to generate high-quality future states of fluids, while enhancing the model's generalization ability by perceiving parameters in various physical setups. Extensive experiments on multiple benchmark datasets have verified the effectiveness and robustness of the ST-PAD framework, which showcase that ST-PAD outperforms current mainstream models in fluid dynamics modeling and prediction, especially in effectively capturing local representations and maintaining significant advantages in OOD generations.