{"ID":2849037,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24279","arxiv_id":"2510.24279","title":"HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves","abstract":"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.","short_abstract":"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 proble...","url_abs":"https://arxiv.org/abs/2510.24279","url_pdf":"https://arxiv.org/pdf/2510.24279v1","authors":"[\"Matteo Calafà\",\"Yuanxin Xia\",\"Cheol-Ho Jeong\"]","published":"2025-10-28T10:39:10Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CE\",\"cs.LG\",\"eess.AS\"]","methods":"[]","has_code":false}