When physics meets machine learning: a survey of physics-informed machine learning
Chuizheng Meng, Sam Griesemer, Defu Cao, Sungyong Seo, Yan Liu
Abstract
Abstract Physics-informed machine learning (PIML), the combination of prior physics knowledge with data-driven machine learning models, has emerged as an effective means of mitigating a shortage of training data, increasing model generalizability, and ensuring physical plausibility of results. In this paper, we survey a wide variety of recent works in PIML and summarize them from three key aspects: 1) motivations of PIML, 2) physics knowledge in PIML, and 3) methods of physics knowledge integration in PIML. We additionally discuss current challenges and corresponding research opportunities in PIML.
Topics & Concepts
Physics educationArtificial intelligenceMachine learningPhysicsMathematics educationComputer sciencePsychologyComputational Physics and Python ApplicationsModel Reduction and Neural NetworksGaussian Processes and Bayesian Inference