用新框架提升多物理场模拟的精度与效率。
COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations
- 引入递归与注意力机制建模物理过程间的复杂关联。
- 在多个复杂系统上预测精度超越现有最先进方法。
- 可适配多种神经算子框架，适合科学计算领域研究者。
多物理场模拟在科学与工程领域对复杂交互的精准建模至关重要。尽管神经算子（尤其是傅里叶神经算子，FNO）显著提升了计算效率，但往往难以有效捕捉耦合物理过程中的内在复杂相关性。为此，我们提出COMPOL——一种新型的耦合多物理场算子学习框架。COMPOL通过引入复杂的递归与基于注意力的聚合机制，在潜在特征空间中有效建模相互作用物理过程之间的依赖关系。该方法具有架构无关性，可无缝集成至涉及潜在空间变换的各种神经算子框架中。在多种基准测试上（包括生物反应-扩散系统、模式形成化学反应、多相地质流动以及热-水-力-机械过程）的大量实验表明，COMPOL始终在预测精度上优于当前最先进的方法。
原文摘要 · Abstract (English)
Multiphysics simulations play an essential role in accurately modeling complex interactions across diverse scientific and engineering domains Although neural operators especially the Fourier Neural Operator FNO have significantly improved computational efficiency they often fail to effectively capture intricate correlations inherent in coupled physical processes To address this limitation we introduce COMPOL a novel coupled multiphysics operator learning framework COMPOL extends conventional operator architectures by incorporating sophisticated recurrent and attentionbased aggregation mechanisms effectively modeling interdependencies among interacting physical processes within latent feature spaces Our approach is architectureagnostic and seamlessly integrates into various neural operator frameworks that involve latent space transformations Extensive experiments on diverse benchmarksincluding biological reactiondiffusion systems patternforming chemical reactions multiphase geological flows and thermohydromechanical processes demonstrate that COMPOL consistently achieves superior predictive accuracy compared to stateoftheart methods.
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