Scalable algorithms for physics-informed neural and graph networks
Scalable algorithms for physics-informed neural and graph networks
Abstract Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, …