APPLICATION OF PHYSICS-BASED MACHINE LEARNING TO CIVIL ENGINEERING

Abstract

This paper reports on the activities of the Physical Model + AI Working Group established within the AI and Data Science Practical Research Subcommittee of the Structural Engineering Committee in the Japan Society of Civil Engineers. Aiming to integrate AI technology and physics, this WG conducted research mainly on a method called Physics-Informed Neural Networks (PINNs), which has attracted particular attention in recent years. Specifically, the working group conducted a literature survey on PINNs, examined the learning convergence of PINNs, applied PINNs to the analysis of marine tsunami propagation, performed inverse analysis of wave propagation problems using PINNs, and applied neural networks as surrogate models to mechanical problems. This paper provides an overview of the results of these studies.