GenCP: Towards Generative Modeling Paradigm of Coupled Physics
This addresses the problem of low efficiency and fidelity in simulating strongly coupled spatio-temporal physical systems for researchers and practitioners in computational physics and AI for science.
The paper tackles the challenge of simulating complex coupled physical systems by proposing GenCP, a generative modeling paradigm that integrates probability density evolution with iterative multiphysics coupling, enabling training on decoupled data and inference of coupled physics, and demonstrates superior performance on synthetic and multi-physics scenarios.
Real-world physical systems are inherently complex, often involving the coupling of multiple physics, making their simulation both highly valuable and challenging. Many mainstream approaches face challenges when dealing with decoupled data. Besides, they also suffer from low efficiency and fidelity in strongly coupled spatio-temporal physical systems. Here we propose GenCP, a novel and elegant generative paradigm for coupled multiphysics simulation. By formulating coupled-physics modeling as a probability modeling problem, our key innovation is to integrate probability density evolution in generative modeling with iterative multiphysics coupling, thereby enabling training on data from decoupled simulation and inferring coupled physics during sampling. We also utilize operator-splitting theory in the space of probability evolution to establish error controllability guarantees for this "conditional-to-joint" sampling scheme. We evaluate our paradigm on a synthetic setting and three challenging multi-physics scenarios to demonstrate both principled insight and superior application performance of GenCP. Code is available at this repo: github.com/AI4Science-WestlakeU/GenCP.