  This paper presents a novel approach called the boundary integrated neural
networks (BINNs) for analyzing acoustic radiation and scattering. The method
introduces fundamental solutions of the time-harmonic wave equation to encode
the boundary integral equations (BIEs) within the neural networks, replacing
the conventional use of the governing equation in physics-informed neural
networks (PINNs). This approach offers several advantages. Firstly, the input
data for the neural networks in the BINNs only require the coordinates of
"boundary" collocation points, making it highly suitable for analyzing acoustic
fields in unbounded domains. Secondly, the loss function of the BINNs is not a
composite form, and has a fast convergence. Thirdly, the BINNs achieve
comparable precision to the PINNs using fewer collocation points and hidden
layers/neurons. Finally, the semi-analytic characteristic of the BIEs
contributes to the higher precision of the BINNs. Numerical examples are
presented to demonstrate the performance of the proposed method.
