Trained DeepONet models for predicting the sound field in 2D and 3D domains

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of
sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations.

The data in this repository consist of the trained DeepONet models for reproducing all results in the paper "Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators", by Borrel-Jensen et al. The trained models provided are for the cubic, L-shaped, furnished and dome geometries in 3D and for the transfer learning results in 2D.