An Unsupervised Scientific Machine Learning Algorithm for Approximating Displacement of Object in Mass-Spring-Damper Systems

Differential equations play a significant role in modeling of real world dynamical problems. A large amount of prior physical information in the form of differential equations are inherited in the dynamical systems. However, the black box machine learning models fail to express insightful scientific information from the data. Physics-informed neural networks (PINNs) can bridge the gap between scientific computing and black box models. This paper exploits a new application of PINNs for approximating the displacement of an object in mass-spring-damper systems. 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 literature.