2025/10/28 by Calafà, Matteo, Xia, Yuanxin, Jeong, Cheol-Ho
#Audio and Speech Processing (eess.AS) #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Machine Learning (cs.LG) #Sound (cs.SD) #and Science (cs.CE) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2510.24279
We present a novel neural network architecture for the efficient prediction of sound fields in two and three dimensions. The network is designed to automatically satisfy the Helmholtz equation, ensuring that the outputs are physically valid. Therefore, the method can effectively learn solutions to boundary-value problems in various wave phenomena, such as acoustics, optics, and electromagnetism. Numerical experiments show that the proposed strategy can potentially outperform state-of-the-art methods in room acoustics simulation, in particular in the range of mid to high frequencies.