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This research introduces COMPOL, a neural operator framework that addresses the limitations of existing models like FNO in capturing correlations within coupled multiphysics systems. By integrating recurrent and attention-based aggregation mechanisms into latent feature spaces, it achieves superior predictive accuracy across diverse benchmarks ranging from chemical reactions to geological flows. The work represents a significant advancement in applying machine learning operators to complex scientific simulations where traditional methods struggle with interdependencies among physical processes.
COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations
arXiv:2501.17296v4 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2501.17296 [cs.LG]
(or arXiv:2501.17296v4 [cs.LG] for this version)
Full text under the publisher's open licence · 142 words
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