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

Autor:	Borrel-Jensen, Nikolas, Engsig-Karup, Allan P., Jeong, Cheol-Ho
Rok vydání:	2021
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing Physics - Computational Physics
Zdroj:	Jasa Express Letters 2021, Volume 1, Issue 12, pp. 122402
Druh dokumentu:	Working Paper
DOI:	10.1121/10.0009057
Popis:	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. Comment: 11 pages, 5 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.11313 Zobrazit plný text záznamu View this record from Arxiv