  Realistic sound is essential in virtual environments, such as computer games
and mixed reality. Efficient and accurate numerical methods for pre-calculating
acoustics have been developed over the last decade; however, pre-calculating
acoustics makes handling dynamic scenes with moving sources challenging,
requiring intractable memory storage. A physics-informed neural network (PINN)
method in 1D is presented, which learns a compact and efficient surrogate model
with parameterized moving Gaussian sources and impedance boundaries, and
satisfies a system of coupled equations. The model shows relative mean errors
below 2%/0.2 dB and proposes a first step in developing PINNs for realistic 3D
scenes.
