HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
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Abstract (arXiv)
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.
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References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2022Neural Conservation Laws: A Divergence-Free Perspectivecited by 70
- 2019Machine learning in acoustics: theory and applicationscited by 36
- 2023Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operatorscited by 12
- 2021Physics-informed neural networks for one-dimensional sound field predictions with parameterized sources and impedance boundariescited by 7
- 2024Physics-Informed Holomorphic Neural Networks (PIHNNs): Solving Linear Elasticity Problemscited by 7
- 2025A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domainscited by 2
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- 2026Quadrature-Aware Complex-Linear Neural Operator for Boundary-to-Field Prediction in Resonant Acoustics
- 2026A Probabilistic Framework for Solving High-Frequency Helmholtz Equations via Diffusion Models
- 2023Reconstruction of Sound Field through Diffusion Models
- 2024Urban Sound Propagation: a Benchmark for 1-Step Generative Modeling of Complex Physical Systems
- 2025Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening
- 2026ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion