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nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications., , , and . J. Comput. Phys., (2020)A new definition of fractional Laplacian with application to modeling three-dimensional nonlocal heat conduction., and . J. Comput. Phys., (2016)A fast semi-discrete Kansa method to solve the two-dimensional spatiotemporal fractional diffusion equation., , , , and . J. Comput. Phys., (2017)On numerical methods to second-order singular initial value problems with additive white noise., , and . J. Comput. Appl. Math., (2022)Nonlocal Physics-Informed Neural Networks - A Unified Theoretical and Computational Framework for Nonlocal Models., , , and . AAAI Spring Symposium: MLPS, volume 2587 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)Neural-net-induced Gaussian process regression for function approximation and PDE solution., , and . CoRR, (2018)Space-fractional advection-dispersion equations by the Kansa method., , and . J. Comput. Phys., (2015)Gauss-Jacobi-type quadrature rules for fractional directional integrals., , and . Comput. Math. Appl., 66 (5): 597-607 (2013)fPINNs: Fractional Physics-Informed Neural Networks., , and . SIAM J. Sci. Comput., 41 (4): A2603-A2626 (2019)Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators., , , , and . Nat. Mach. Intell., 3 (3): 218-229 (2021)