Dynamic Analysis of One-Dimensional Continuum based on Physics-Informed Neural Networks

Surrogate models, which reproduce the inputs and outputs of physical phenomena in a data-driven man- ner, 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 Net- works (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.