5 papers
Physics-Informed Neural Operator for Speech Production Analysis
Kazuya Yokota, Xinmeng Luan, Debasish Ray Mohapatra +2
Physics-informed neural operators (PINOs) have recently gained attention as fast numerical simulators with potential for solving inverse problems. This study proposes the first PIN…
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 Characterization of the Resonator in the Chinese Transverse Flute (dizi)
Xinmeng Luan, Song Wang, Gary Scavone +1
The dizi is a traditional Chinese transverse flute and is most distinguished from the western flute by the presence of a hole covered by a wrinkled membrane. In this study, we anal…
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…