GenCP: Towards Generative Modeling Paradigm of Coupled Physics

2026-01-27

AI Summary

Existing methods for modeling the spatiotemporal dynamics of strongly coupled multiphysics systems struggle to balance computational efficiency and fidelity while relying on costly coupled data. This work reframes coupled physics modeling as a probabilistic inference problem and introduces a novel “conditional-to-joint” sampling paradigm by integrating generative modeling with iterative multiphysics coupling mechanisms for the first time. Leveraging operator splitting theory, the approach provides rigorous error controllability guarantees. Notably, it enables training and inference of coupled system behavior using only decoupled data. Experiments on synthetic benchmarks and three complex multiphysics scenarios demonstrate substantial improvements in both simulation efficiency and fidelity, confirming the method’s theoretical advantages and superior performance.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• coupled physics
• multiphysics simulation
• generative modeling
• spatio-temporal systems
• decoupled data

Innovation

• generative modeling
• coupled multiphysics
• probability density evolution
• operator splitting
• conditional-to-joint sampling