Towards Physics-Informed Neural Networks for Stiff Guitar String Vibrations

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for simulating transverse vibration of one-dimensional linear stiff guitar strings. The study addresses the challenge posed by the dispersive and high-frequency characteristics of stiff string vibrations. The governing Partial Differential Equation along with associated initial and boundary conditions (induced by plucking) are directly incorporated into the neural network loss function, eliminating the need for labeled training data.