  This work presents a physics-driven machine learning framework for the
simulation of acoustic scattering problems. The proposed framework relies on a
physics-informed neural network (PINN) architecture that leverages prior
knowledge based on the physics of the scattering problem as well as a tailored
network structure that embodies the concept of the superposition principle of
linear wave interaction. The framework can also simulate the scattered field
due to rigid scatterers having arbitrary shape as well as high-frequency
problems. Unlike conventional data-driven neural networks, the PINN is trained
by directly enforcing the governing equations describing the underlying
physics, hence without relying on any labeled training dataset. Remarkably, the
network model has significantly lower discretization dependence and offers
simulation capabilities akin to parallel computation. This feature is
particularly beneficial to address computational challenges typically
associated with conventional mesh-dependent simulation methods. The performance
of the network is investigated via a comprehensive numerical study that
explores different application scenarios based on acoustic scattering.
