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