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.

翻译：现实世界中的物理系统本质上是复杂的，通常涉及多种物理过程的耦合，这使得其仿真既具有重要价值又极具挑战性。许多主流方法在处理解耦数据时面临困难。此外，它们在强耦合的时空物理系统中也存在效率和保真度低下的问题。本文提出GenCP，一种新颖且优雅的耦合多物理场仿真生成式范式。通过将耦合物理建模表述为概率建模问题，我们的核心创新在于将生成式建模中的概率密度演化与迭代的多物理场耦合过程相融合，从而能够在解耦仿真数据上进行训练，并在采样过程中推断耦合物理。我们还利用概率演化空间中的算子分裂理论，为这种“条件到联合”的采样方案建立了误差可控性保证。我们在一个合成场景和三个具有挑战性的多物理场场景上评估了该范式，结果展示了GenCP在原理洞察力和实际应用性能上的双重优势。代码已在此仓库开源：github.com/AI4Science-WestlakeU/GenCP。