The objective of this project is to develop a methodology to predict the sound field in a room, based on a set of sparse measurements distributed about the room. We plan to explore the use of Physics Informed Neural Networks (PINNs) for the task of reconstructing the sound field, based on a set of sparse measurements. The main outcomes of this project are threefold:
To curate a data set for the training of neural networks in reverberant environments.
To examine the predictive potential of PINNs to interpolate and extrapolate acoustic data in fine spatial grids.
Examine the ability of the network to model the propagation of sound in free-fields, which is relevant for sound radiation and noise control.