Integrating Physics-Based Modeling with Machine Learning: A Survey 论文

2020引用 307
Model Reduction and Neural NetworksComputational Physics and Python ApplicationsGaussian Processes and Bayesian Inference

摘要

In this manuscript, we provide a structured and comprehensive overview of techniques to integrate machine learning with physics-based modeling. First, we provide a summary of application areas for which these approaches have been applied. Then, we describe classes of methodologies used to construct physics-guided machine learning models and hybrid physics-machine learning frameworks from a machine learning standpoint. With this foundation, we then provide a systematic organization of these existing techniques and discuss ideas for future research.