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Title:

Physics-informed neural networks for acoustic boundary admittance estimation

Document type:
Zeitschriftenaufsatz
Author(s):
Schmid, Johannes D.; Bauerschmidt, Philipp; Gurbuz, Caglar; Eser, Martin; Marburg, Steffen
Abstract:
Acoustic simulations often face significant uncertainties due to limited knowledge of acoustic boundary conditions. While measuring the boundary admittance in situ is challenging in practical applications, numerical inverse methods can be used to characterize the boundary conditions based on sound pressure data. However, conventional inverse methods require a validated forward model and can become impractical for computationally expensive simulation models. Over the past years, machine learning...     »
Keywords:
Physics-informed neural networks, Machine learning, Data-driven methods, Inverse problems, Computational acoustics
Journal title:
Mechanical Systems and Signal Processing
Year:
2024
Journal volume:
215
Pages contribution:
111405
Fulltext / DOI:
doi:https://doi.org/10.1016/j.ymssp.2024.111405
WWW:
https://www.sciencedirect.com/science/article/pii/S0888327024003030
Print-ISSN:
0888-3270
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