Permutation-Invariant Physics-Informed Neural Network for Region-to-Region Sound Field Reconstruction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918308246192128 |
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| author | Chen, Xingyu Zhao, Sipei Ma, Fei Cheng, Eva Burnett, Ian S. |
| author_facet | Chen, Xingyu Zhao, Sipei Ma, Fei Cheng, Eva Burnett, Ian S. |
| contents | Most existing sound field reconstruction methods target point-to-region reconstruction, interpolating the Acoustic Transfer Functions (ATFs) between a fixed-position sound source and a receiver region. The applicability of these methods is limited because real-world ATFs tend to varying continuously with respect to the positions of sound sources and receiver regions. This paper presents a permutation-invariant physics-informed neural network for region-to-region sound field reconstruction, which aims to interpolate the ATFs across continuously varying sound sources and measurement regions. The proposed method employs a deep set architecture to process the receiver and sound source positions as an unordered set, preserving acoustic reciprocity. Furthermore, it incorporates the Helmholtz equation as a physical constraint to guide network training, ensuring physically consistent predictions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_19491 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Permutation-Invariant Physics-Informed Neural Network for Region-to-Region Sound Field Reconstruction Chen, Xingyu Zhao, Sipei Ma, Fei Cheng, Eva Burnett, Ian S. Audio and Speech Processing Most existing sound field reconstruction methods target point-to-region reconstruction, interpolating the Acoustic Transfer Functions (ATFs) between a fixed-position sound source and a receiver region. The applicability of these methods is limited because real-world ATFs tend to varying continuously with respect to the positions of sound sources and receiver regions. This paper presents a permutation-invariant physics-informed neural network for region-to-region sound field reconstruction, which aims to interpolate the ATFs across continuously varying sound sources and measurement regions. The proposed method employs a deep set architecture to process the receiver and sound source positions as an unordered set, preserving acoustic reciprocity. Furthermore, it incorporates the Helmholtz equation as a physical constraint to guide network training, ensuring physically consistent predictions. |
| title | Permutation-Invariant Physics-Informed Neural Network for Region-to-Region Sound Field Reconstruction |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.19491 |