Multi-output physics-informed neural network for one- and two-dimensional nonlinear time distributed-order models
DOI10.3934/nhm.2023080OpenAlexW4388904742MaRDI QIDQ6186170
Yining Yang, Wen-Kai Liu, Yang Liu, Hong Li
Publication date: 2 February 2024
Published in: Networks and Heterogeneous Media (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/nhm.2023080
Crank-Nicolson schemeFBN-\(\theta\) methodmulti-output neural networknonlinear-time distributed-order models
Artificial neural networks and deep learning (68T07) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Fractional derivatives and integrals (26A33) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Initial value problems for nonlinear higher-order PDEs (35G25) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70) Fractional partial differential equations (35R11)
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