2025/07/16 by Carlos De La Vega Martin, Martin, Carlos De La Vega, Rebeca Fernandez‐Ruiz +3
Computer Science · Engineering · #Advanced Theoretical and Applied Studies in Material Sciences and Geometry #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Manufacturing Process and Optimization #Neural Networks and Applications #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2507.12563
openalex publication_date 2025/07/16 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28
Physical modelling synthesis aims to generate audio from physical simulations of vibrating structures. Thin elastic plates are a common model for drum membranes. Traditional numerical methods like finite differences and finite elements offer high accuracy but are computationally demanding, limiting their use in real-time audio applications. This paper presents a comparative analysis of neural network-based approaches for solving the vibration of nonlinear elastic plates. We evaluate several state-of-the-art models, trained on short sequences, for prediction of long sequences in an autoregressive fashion. We show some of the limitations of these models, and why is not enough to look at the prediction error in the time domain. We discuss the implications for real-time audio synthesis and propose future directions for improving neural approaches to model nonlinear vibration.