Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks
DOI10.1016/j.cma.2024.117448MaRDI QIDQ6643609
Zheyuan Hu, George Em. Karniadakis, Zhongqiang Zhang, Kenji Kawaguchi
Publication date: 26 November 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
curse of dimensionalityhigh-dimensional PDEsphysics-informed neural networksfractional and tempered fractional PDEs
Monte Carlo methods (65C05) Initial-boundary value problems for second-order parabolic equations (35K20) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70) Numerical quadrature and cubature formulas (65D32) Fractional partial differential equations (35R11) Initial-boundary value problems for linear higher-order PDEs (35G16)
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