Physics-informed neural networks for localized assessment in Euler-Bernoulli beams

Abstract

This study employs the Physics-Informed Neural Networks (PINNs) to address an inverse problem, specifically evaluating local changes in material properties within an Euler-Bernoulli beam through numerical simulations. In our numerical model, a viscoelastic material is strategically introduced to a localized zone of the beam, inducing non-constant and complex alterations in the storage modulus. The training process incorporates the partial differential equation into the neural network's loss function, coupled with essential stability conditions for the machine learning model, enhancing the efficiency of the training process. Notably, this methodology excels in characterizing structural behavior influenced by these intricate local changes in material properties, bypassing the need to identify the entire structure and consequently to deal with uncertainties related to boundary conditions. The effectiveness of the approach is validated through numerical simulations, underscoring its potential for applications in efficiently assessing and understanding local material alterations within complex structural systems.