用平面波叠加构建快速声场预测神经模型
HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
- 通过平面波叠加设计满足波动方程的神经网络
- 中高频声学模拟性能优于现有方法
- 适合需要物理一致性模拟的声学/光学研究
我们提出一种新型神经网络架构，用于高效预测二维和三维声场。该网络设计可自动满足亥姆霍兹方程，确保输出具有物理合理性。因此，该方法能有效学习各类波现象（如声学、光学、电磁学）的边界值问题解。数值实验表明，该策略在房间声学模拟中，特别是在中高频范围，可能超越当前最先进方法。
原文摘要 · Abstract (English)
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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