2 papers
math.NA2024
A forward scheme with machine learning for forward-backward SDEs with jumps by decoupling jumps
Reiichiro Kawai, Riu Naito, Toshihiro Yamada
Forward-backward stochastic differential equations (FBSDEs) have been generalized by introducing jumps for better capturing random phenomena, while the resulting FBSDEs are far mor…
math.NA2020
A machine learning solver for high-dimensional integrals: Solving Kolmogorov PDEs by stochastic weighted minimization and stochastic gradient descent through a high-order weak approximation scheme of SDEs with Malliavin weights
Riu Naito, Toshihiro Yamada
The paper introduces a very simple and fast computation method for high-dimensional integrals to solve high-dimensional Kolmogorov partial differential equations (PDEs). The new ma…