Towards Physics-Informed Neural Networks for Stiff Guitar String Vibrations
Abstract
Modeling stiff string vibrations is challenging due to their dispersive and high-frequency characteristics. This study investigates the effectiveness of Physics-Informed Neural Networks (PINNs) in simulating the transverse vibration of a one-dimensional linear stiff string with sharp initial conditions induced by plucking. The governing Partial Differential Equation (PDE), along with the associated initial and boundary conditions, is incorporated directly into the loss function of the neural network. For the reference measurements, a wire-breaking experiment was performed to excite the string, and its vibration response was captured using a laser profiler. The comparison between experimental measurements, finite-difference time-domain (FDTD) simulations, and PINN-based simulations shows good overall agreement, highlighting the potential of PINNs for modeling stiff-string vibrations.
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BibTeX
@misc{luan2026towards,
title = {{Towards Physics-Informed Neural Networks for Stiff Guitar String Vibrations}},
author = {Xinmeng Luan and Kensuke Okada and Ryoya Tabata and Gary Scavone},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21795},
eprint = {2609.21795},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21795},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Towards Physics-Informed Neural Networks for Stiff Guitar String Vibrations
AU - Xinmeng Luan
AU - Kensuke Okada
AU - Ryoya Tabata
AU - Gary Scavone
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21795
UR - https://arxiv.org/abs/2609.21795
AB - Modeling stiff string vibrations is challenging due to their dispersive and high-frequency characteristics. This study investigates the effectiveness of Physics-Informed Neural Networks (PINNs) in simulating the transverse vibration of a one-dimensional linear stiff string with sharp initial conditions induced by plucking. The governing Partial Differential Equation (PDE), along with the associated initial and boundary conditions, is incorporated directly into the loss function of the neural network. For the reference measurements, a wire-breaking experiment was performed to excite the string, and its vibration response was captured using a laser profiler. The comparison between experimental measurements, finite-difference time-domain (FDTD) simulations, and PINN-based simulations shows good overall agreement, highlighting the potential of PINNs for modeling stiff-string vibrations.
ER -