COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations
This addresses the need for more accurate and scalable multiphysics simulations in scientific and engineering domains, representing a novel method for a known bottleneck.
The paper tackles the problem of neural operators failing to capture intricate correlations in coupled multiphysics simulations by introducing COMPOL, a framework that incorporates recurrent and attention-based mechanisms to model interdependencies, achieving superior predictive accuracy across diverse benchmarks.
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.