用生成模型统一模拟多物理耦合系统，训练时可分离数据，推理时自动耦合。
GenCP: Towards Generative Modeling Paradigm of Coupled Physics
- 将耦合物理建模转化为概率分布建模，结合迭代耦合机制。
- 可在解耦数据上训练，推断时实现强耦合系统的高保真模拟。
- 理论保障采样误差可控，适合多物理系统仿真与科学研究。
真实世界的物理系统通常涉及多个物理过程的耦合，其模拟既重要又困难。主流方法在处理解耦数据时存在挑战，且在强耦合时空系统中效率和精度不足。本文提出GenCP，一种新颖的耦合多物理生成建模范式。通过将耦合物理建模视为概率建模问题，核心创新在于将概率密度演化融入生成模型，并结合迭代多物理耦合，实现仅用解耦仿真数据训练，采样时推断耦合物理行为。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”采样方案建立误差可控性保证。在合成场景及三个复杂多物理场景中评估，验证了GenCP在理论严谨性和应用性能上的优越性。代码已开源：github.com/AI4Science-WestlakeU/GenCP。
原文摘要 · Abstract (English)
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.
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