Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations
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Publication:2083146
DOI10.1016/j.cma.2022.115523OpenAlexW4296060623WikidataQ114196703 ScholiaQ114196703MaRDI QIDQ2083146
Ling Guo, Hao Wu, Xiaochen Yu, Tao Zhou
Publication date: 10 October 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2203.08501
Related Items (9)
A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations ⋮ Efficient Monte Carlo Method for Integral Fractional Laplacian in Multiple Dimensions ⋮ Adaptive deep density approximation for fractional Fokker-Planck equations ⋮ Bi-Orthogonal fPINN: A Physics-Informed Neural Network Method for Solving Time-Dependent Stochastic Fractional PDEs ⋮ MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling ⋮ Failure-Informed Adaptive Sampling for PINNs ⋮ Multi-output physics-informed neural network for one- and two-dimensional nonlinear time distributed-order models ⋮ Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions ⋮ An overview on deep learning-based approximation methods for partial differential equations
Uses Software
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