HergNet: a Fast Neural Surrogate Model for Sound Field Predictions via Superposition of Plane Waves
By: Matteo Calafà, Yuanxin Xia, Cheol-Ho Jeong
Potential Business Impact:
Makes sound simulations faster and more real.
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.
Similar Papers
Generative Models for Helmholtz Equation Solutions: A Dataset of Acoustic Materials
Machine Learning (CS)
Makes sound simulations super fast for new materials.
Convergence of physics-informed neural networks modeling time-harmonic wave fields
Computational Engineering, Finance, and Science
Makes computers better at predicting sound in rooms.
Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks
Machine Learning (CS)
Helps computers predict sound waves better.