1 citations · 1 across the 5 of their papers we have counts for
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Towards Physics-Informed Neural Networks for Stiff Guitar String Vibrations
Xinmeng Luan, Kensuke Okada, Ryoya Tabata +1
Modeling stiff string vibrations is challenging due to their dispersive and high-frequency characteristics. This study investigates the effectiveness of Physics-Informed Neural Net…
Inverse Problems in Musical Instrument Modeling: A Structured Taxonomy and Review
Xinmeng Luan, Gary Scavone
Inverse problems arise in a wide range of applications in musical acoustics, including physics-based sound synthesis, musical instrument modeling, design, and optimization. However…
Masked Wavelet Scattering Transform Neural Field for Sound Field Reconstruction
Xinmeng Luan, Samuel A. Verburg, Efren Fernandez-Grande +1
In this paper, we propose a reconstruction framework that leverages the Wavelet Scattering Transform (WST) as a multi-scale feature extractor to impose statistical priors under spa…
Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction
Xinmeng Luan, Gary Scavone
This study investigates the use of an unsupervised, physics-informed deep learning framework to model a one-degree-of-freedom mass-spring system subjected to a nonlinear friction b…
Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks
Xinmeng Luan, Kazuya Yokota, Gary Scavone
This study investigates the application of Physics-Informed Neural Networks (PINNs) to inverse problems in acoustic tube analysis, focusing on reconstructing acoustic fields from n…