Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures

arXiv:2402.17992v1 Announce Type: cross
Abstract: There is a growing interest in utilizing machine learning (ML) methods for structural metamodeling due to the substantial computational cost of traditional numerical simulations. The existing data-driven strategies show potential limitations to the model robustness and interpretability as well as the dependency of rich data. To address these challenges, this paper presents a novel physics-informed machine learning (PiML) method, which incorporates scientific principles and physical laws into deep neural networks for modeling seismic responses of …

abstract arxiv computational cost cs.lg data data-driven interpretability limitations machine machine learning model robustness numerical physics physics.app-ph physics-informed prediction robustness show simulations strategies type