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- W4200123642 abstract "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 one dimension 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 three-dimensional scenes." @default.
- W4200123642 created "2021-12-31" @default.
- W4200123642 date "2021-12-27" @default.
- W4200123642 modified "2023-10-18" @default.
- W4200123642 title "10.1121/10.0009057.2" @default.
- W4200123642 doi "https://doi.org/10.1121/10.0009057.2" @default.
- W4200123642 hasPublicationYear "2021" @default.
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