Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems

Dynamical problems are generally governed by a set of linear/non-linear differential equations (DEs). A large amount of prior physical information in the form of DEs plays an important role in simulation of dynamical systems. However, the traditional data-hungry machine learning models fail to express insightful scientific information from the data. Most of the implementations of neural networks are to perform non-linear mapping from input space to target space. However, Physics-informed neural networks (PINNs) can bridge the gap between scientific computing and data-hungry models. This paper exploits a new application of PINNs for approximating the behaviour of mass-spring-damper systems, showcasing how PINNs can effectively blend scientific principles with data-driven modeling. In this regard, we present solutions of two realistic application problems using PINNs. The accuracy of the predicted displacements of objects is established through results from literatures.