Bi-Orthogonal fPINN: A Physics-Informed Neural Network Method for Solving Time-Dependent Stochastic Fractional PDEs
DOI10.4208/cicp.oa-2023-0075arXiv2303.10913OpenAlexW4388616107MaRDI QIDQ6143622
Fanhai Zeng, George Em. Karniadakis, Lei Ma, Rongxin Li, Ling Guo
Publication date: 5 January 2024
Published in: Communications in Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2303.10913
inverse problemsuncertainty quantificationstochastic fractional differential equationsscientific machine learningPINNs
Computational learning theory (68Q32) Artificial neural networks and deep learning (68T07) Monte Carlo methods (65C05) Fractional derivatives and integrals (26A33) Inverse problems for PDEs (35R30) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs (65M12) Error bounds for initial value and initial-boundary value problems involving PDEs (65M15) PDEs with randomness, stochastic partial differential equations (35R60) Numerical quadrature and cubature formulas (65D32) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32) Fractional partial differential equations (35R11) PDE constrained optimization (numerical aspects) (49M41)
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