Deep neural networks for waves assisted by the Wiener-Hopf method.

ABSTRACT: In this work, the classical Wiener-Hopf method is incorporated into the emerging deep neural networks for the study of certain wave problems. The essential idea is to use the first-principle-based analytical method to efficiently produce a large volume of datasets that would supervise the learning of data-hungry deep neural networks, and to further explain the working mechanisms on underneath. To demonstrate such a combinational research strategy, a deep feed-forward network is first used to approximate the forward propagation model of a duct acoustic problem, which can find important aerospace applications in aeroengine noise tests. Next, a convolutional type U-net is developed to learn spatial derivatives in wave equations, which could help to promote computational paradigm in mathematic

SUBMITTER: Huang X 

PROVIDER: S-EPMC7125989 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature