  Recently deep learning and machine learning approaches have been widely
employed for various applications in acoustics. Nonetheless, in the area of
sound field processing and reconstruction classic methods based on the
solutions of wave equation are still widespread. Recently, physics-informed
neural networks have been proposed as a deep learning paradigm for solving
partial differential equations which govern physical phenomena, bridging the
gap between purely data-driven and model based methods. Here, we exploit
physics-informed neural networks to reconstruct the early part of missing room
impulse responses in an uniform linear array. This methodology allows us to
exploit the underlying law of acoustics, i.e., the wave equation, forcing the
neural network to generate physically meaningful solutions given only a limited
number of data points. The results on real measurements show that the proposed
model achieves accurate reconstruction and performance in line with respect to
state-of-the-art deep-learning and compress sensing techniques while
maintaining a lightweight architecture.
