<p>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:</p>
<ul>
<li>To curate a data set for the training of neural networks in reverberant environments.</li>
<li>To examine the predictive potential of PINNs to interpolate and extrapolate acoustic data in fine spatial grids.</li>
<li>Examine the ability of the network to model the propagation of sound in free-fields, which is relevant for sound radiation and noise control.</li>
</ul>