Physics-Informed Neural Networks (PINNs) for Sound Field Predictions with Parameterized Sources and Impedance Boundaries
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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 and requires intractable memory storage. A Physics-Informed Neural Networks (PINNS) method is presented, learning a compact and efficient surrogate model with parameterized moving sources and impedance boundaries, satisfying a system of coupled equations. The trained model shows relative mean errors below 2 dB, indicating that acoustics with moving sources and impedance boundaries can be predicted in real-time using PINNs.
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