Equivalence Between Schwarzschild and Wiener–Hopf Solutions for Flat Plate Trailing-Edge Scattering

Chen, Yihao; Yang, Yue; Huang, Xun

doi:10.2514/1.j063321

AIAA Journal

2023

DOI: 10.2514/1.j063321

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Equivalence Between Schwarzschild and Wiener–Hopf Solutions for Flat Plate Trailing-Edge Scattering

Yihao Chen,

Yue Yang,

Xun Huang

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2024

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Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates

Liang,

Huang

2024

AIAA Journal

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This paper proposes a mathematics-informed neural network (MINN) approach for resolving the long-term challenge in wave scattering modeling. The central innovation lies in integrating Cauchy–Riemann equations into machine learning architectures. By incorporating Cauchy integrals and boundary conditions, the neural network successfully learns to numerically produce matrix kernel factorization for Wiener–Hopf analytical models. To validate and demonstrate the approach, a benchmark case of wave scattering from parallel hard–soft plates is studied by comparing the machine learning results with the available analytical solutions. The proposed MINN approach could provide a new route to extensively enhance the theoretical modeling capability for several wave scattering and fluid mechanics problems. The code can be found at https://github.com/lscapku/MINN .

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Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates

Liang,

Huang

2024

AIAA Journal

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Equivalence Between Schwarzschild and Wiener–Hopf Solutions for Flat Plate Trailing-Edge Scattering

Cited by 1 publication

References 24 publications

Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates

Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates

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