HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
This work addresses the need for fast and physically valid sound field predictions in acoustics, optics, and electromagnetism, though it appears incremental as it builds on existing neural network approaches with a specific constraint.
The authors tackled the problem of efficiently predicting sound fields in 2D and 3D by developing a neural network that automatically satisfies the Helmholtz equation, resulting in a method that can outperform state-of-the-art techniques in room acoustics simulation, especially at mid to high frequencies.
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.