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Cited article:

A new algorithm for computing path integrals and weak approximation of SDEs inspired by large deviations and Malliavin calculus

Toshihiro Yamada
Applied Numerical Mathematics 187 192 (2023)
https://doi.org/10.1016/j.apnum.2023.02.012

Deep high-order splitting method for semilinear degenerate PDEs and application to high-dimensional nonlinear pricing models

Riu Naito and Toshihiro Yamada
Digital Finance (2023)
https://doi.org/10.1007/s42521-023-00091-z

Deep Kusuoka Approximation: High-Order Spatial Approximation for Solving High-Dimensional Kolmogorov Equations and Its Application to Finance

Riu Naito and Toshihiro Yamada
Computational Economics (2023)
https://doi.org/10.1007/s10614-023-10476-2

Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus

Akihiko Takahashi and Toshihiro Yamada
Partial Differential Equations and Applications 4 (4) (2023)
https://doi.org/10.1007/s42985-023-00240-4

A high order weak approximation for jump-diffusions using Malliavin calculus and operator splitting

Naho Akiyama and Toshihiro Yamada
Monte Carlo Methods and Applications 28 (2) 97 (2022)
https://doi.org/10.1515/mcma-2022-2109

A new efficient approximation scheme for solving high-dimensional semilinear PDEs: Control variate method for Deep BSDE solver

Akihiko Takahashi, Yoshifumi Tsuchida and Toshihiro Yamada
Journal of Computational Physics 454 110956 (2022)
https://doi.org/10.1016/j.jcp.2022.110956

Deep Weak Approximation of SDEs: A Spatial Approximation Scheme for Solving Kolmogorov Equations

Riu Naito and Toshihiro Yamada
International Journal of Computational Methods 19 (08) (2022)
https://doi.org/10.1142/S0219876221420147