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Realistic sound is essential in virtual environments, such as computer gamesand mixed reality. Efficient and accurate numerical methods for pre-calculatingacoustics have been developed over the last decade; however, pre-calculatingacoustics 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 modelwith parameterized moving Gaussian sources and impedance boundaries, andsatisfies a system of coupled equations. The model shows relative mean errorsbelow 2%/0.2 dB and proposes a first step in developing PINNs for realistic 3Dscenes.

Physics-informed neural networks for one-dimensional sound field predictions with parameterized sources and impedance boundaries