Trained PINN models for predicting the sound field in 1D domains including reference solutions
The data are used to reproduce the results from the paper "Physics-informed neural networks for one-dimensional sound field predictions with parameterized sources and impedance boundaries" by Borrel-Jensen et al.
Realistic sound is essential in virtual environments, such as computer games and mixed reality. Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade; however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging, requiring intractable memory storage. A physics-informed neural network (PINN) method in 1D is presented, which learns a compact and efficient surrogate model with parameterized moving Gaussian sources and impedance boundaries, and satisfies a system of coupled equations. The model shows relative mean errors below 2%/0.2 dB and proposes a first step in developing PINNs for realistic 3D scenes.
The data contains:
* Reference solutions in HDF5 format for validating the predictions
* Trained models used in the result section of the paper
Code is available here: