Machine Learning-Driven Framework for Identifying Parameter-Driven Anomalies in Multiphysics Simulations

This paper addresses the critical challenges associated with error management in multiphysics simulations, particularly regarding the sensitivity of these systems to parameter selection, which can lead to convergence failures and anomalies in simulation outputs. We propose a comprehensive analytical framework that systematically identifies the relationships between simulation parameters and governing equations, enabling the analysis of resulting anomalies. The framework classifies these anomalies, providing insights that inform the selection of appropriate unsupervised machine-learning algorithms for effective anomaly detection. To demonstrate the applicability of this approach, we apply the framework to a heat conjugate transfer (HCT) problem, integrating the heat transfer and Navier-Stokes equations. By thoroughly investigating parameter-driven anomalies, our framework enhances the reliability, convergence, and fidelity of multiphysics simulations, ultimately contributing to the robustness and accuracy of simulation outcomes.