Deep backward schemes for high-dimensional nonlinear PDEs

Deep backward schemes for high-dimensional nonlinear PDEs

5 February 2019

Papers citing "Deep backward schemes for high-dimensional nonlinear PDEs"

15 / 15 papers shown

Title
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training B.-L. Lu Christian Moya Guang Lin PINN 37 11 0 03 Mar 2023
A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations Jiang Yu Nguwi Nicolas Privault 49 5 0 28 Sep 2022
Convergence of a robust deep FBSDE method for stochastic control Kristoffer Andersson Adam Andersson C. Oosterlee 37 19 0 18 Jan 2022
SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems Tingwei Meng Zhen Zhang Jérome Darbon George Karniadakis 32 15 0 14 Jan 2022
Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs Jérome Darbon P. Dower Tingwei Meng 14 22 0 07 May 2021
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes Raffaele Marino N. Macris 29 16 0 09 Dec 2020
Robust pricing and hedging via neural SDEs Patryk Gierjatowicz Marc Sabate Vidales David Siska Lukasz Szpruch Zan Zuric 27 34 0 08 Jul 2020
Space-time deep neural network approximations for high-dimensional partial differential equations F. Hornung Arnulf Jentzen Diyora Salimova AI4CE 34 19 0 03 Jun 2020
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space Nikolas Nusken Lorenz Richter AI4CE 38 105 0 11 May 2020
Extensions of the Deep Galerkin Method A. Al-Aradi Adolfo Correia D. Naiff G. Jardim Yuri F. Saporito 10 27 0 30 Nov 2019
Uniform error estimates for artificial neural network approximations for heat equations Lukas Gonon Philipp Grohs Arnulf Jentzen David Kofler David Siska 37 34 0 20 Nov 2019
Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions Yangang Chen J. Wan 19 59 0 25 Sep 2019
Space-time error estimates for deep neural network approximations for differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philipp Zimmermann 34 33 0 11 Aug 2019
Deep splitting method for parabolic PDEs C. Beck S. Becker Patrick Cheridito Arnulf Jentzen Ariel Neufeld 34 126 0 08 Jul 2019
Unbiased deep solvers for linear parametric PDEs Marc Sabate Vidales David Siska Lukasz Szpruch OOD 32 7 0 11 Oct 2018