Data-Driven Physics-Informed Neural Network for Sound Field Estimation in Rooms of Arbitrary Size

In recent years, many methods for simulating acoustic fields using deep learning have been proposed. In particular, neural networks using physical knowledge (PINN: Physics-Informed Neural Network) have been proposed for the purpose of solving various acoustic problems with higher accuracy. Many of the proposed PINN-based methods have achieved the estimation of a specific sound field. In this study, we propose a data-driven PINN for the estimation of simulated sound fields in rooms of arbitrary size with a single model. By using the coordinates and room size information as the input of PINN, the proposed method demonstrated a notable improvement in estimation accuracy by approximately 5.4 dB compared to the simple data-driven model.