DYNAMIC ANALYSIS OF ONE-DIMENSIONAL CONTINUUM BASED ON PHYSICS-INFORMED NEURAL NETWORKS

Abstract

Surrogate models, which reproduce the inputs and outputs of physical phenomena in a data-driven manner, are increasingly being used as an alternative means of making fast predictions of physical problems, but there is no guarantee that the solutions will satisfy the physical conditions. Physics-Informed Neural Networks (PINNs), on the other hand, are neural networks that solve the governing equations in a data-driven manner by introducing a loss function that represents the constraints imposed by the governing equations. In this paper, we present the formulation and code implementation of PINNs for a one-dimensional continuum free vibration problem.