Navigating speed–accuracy trade-offs for multi-physics simulations

This paper introduces a novel approach aimed at addressing persistent challenges inherent in conventional multiphysics modeling methodologies. Existing techniques, such as numerical modeling and analytical calculations, often suffer from time-consuming and computationally intensive processes, leading to inefficiencies, particularly in intricate simulations. The proposed methodology employs regression machine learning algorithms as a black-box solution to anticipate errors and execution times in multiphysics simulations. Diverging from conventional methods, this approach streamlines the exploration of simulation options, providing discernible choices for balancing speed and precision. The efficacy of the methodology is exemplified through successful applications to heat transfer and fluid–structure interaction problems, illustrating its adaptability across diverse scenarios. Notably, the approach upholds the integrity of physics equations and simulation convergence while markedly reducing the trial-and-error efforts and computational burdens associated with traditional methodologies. In summary, the proposed approach emerges as an innovative and promising solution to augment the accuracy, efficiency, and dependability of multiphysics simulations.