HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915582690983936 |
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| author | Calafà, Matteo Xia, Yuanxin Jeong, Cheol-Ho |
| author_facet | Calafà, Matteo Xia, Yuanxin Jeong, Cheol-Ho |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_24279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves Calafà, Matteo Xia, Yuanxin Jeong, Cheol-Ho Sound Computational Engineering, Finance, and Science Machine Learning Audio and Speech Processing 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. |
| title | HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves |
| topic | Sound Computational Engineering, Finance, and Science Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.24279 |