From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs

28 July 2023

Papers citing "From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs"

7 / 7 papers shown

Title
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning Lorenz Richter Julius Berner 49 19 0 21 Jun 2022
Convergence bounds for nonlinear least squares and applications to tensor recovery Philipp Trunschke 33 7 0 11 Aug 2021
Solving high-dimensional parabolic PDEs using the tensor train format Lorenz Richter Leon Sallandt Nikolas Nusken 54 49 0 23 Feb 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 60 149 0 22 Dec 2020
Deep splitting method for parabolic PDEs C. Beck S. Becker Patrick Cheridito Arnulf Jentzen Ariel Neufeld 54 127 0 08 Jul 2019
Convergence of the Deep BSDE Method for Coupled FBSDEs Jiequn Han Jihao Long 57 158 0 03 Nov 2018
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations M. Raissi 92 186 0 19 Apr 2018