Physics-informed neural networks for inverse problems in structural dynamics

This study introduces an innovative approach that employs Physics-Informed Neural Networks (PINNs) to address inverse problems in structural analysis. Specifically, we apply this technique to the 4-th order PDE of Euler-Bernoulli formulation to approximate beam displacement and identify structural parameters, including damping and elastic modulus. Our methodology incorporates partial differential equations (PDEs) into the neural network's loss function during training, ensuring it adheres to physics-based constraints. This approach simplifies complex structural analysis, even when specific boundary conditions are unavailable. Importantly, our model reliably captures structural behavior without resorting to synthetic noise in data. This study represents a pioneering effort in utilizing PINNs for inverse problems in structural analysis, offering potential inspiration for other fields. The reliable characterization of damping, a typically challenging task, underscores the versatility of methodology. The strategy was initially assessed through numerical simulations utilizing data from a finite element solver and subsequently applied to experimental datasets. The presented methodology successfully identifies structural parameters using experimental data and validates its accuracy against reference data. This work opens new possibilities in engineering problem-solving, positioning Physics-Informed Neural Networks as valuable tools in addressing practical challenges in structural analysis.