Deep splitting method for parabolic PDEs

8 July 2019

Papers citing "Deep splitting method for parabolic PDEs"

23 / 23 papers shown

Title
A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting Kasper Bågmark Adam Andersson S. Larsson Filip Rydin 79 0 0 20 Jan 2025
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Zekun Shi Zheyuan Hu Min Lin Kenji Kawaguchi 203 6 0 27 Nov 2024
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer Georgios Is. Detorakis 28 0 0 21 Aug 2024
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs Ariel Neufeld Philipp Schmocker Sizhou Wu 45 7 0 08 May 2024
A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations Lorenc Kapllani Long Teng 33 2 0 12 Apr 2024
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks Hyeonjung Jung Jung Jayant Gupta B. Jayaprakash Matthew J. Eagon Harish Selvam Carl Molnar W. Northrop Shashi Shekhar AI4CE 35 5 0 26 Apr 2023
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 23 34 0 06 Dec 2022
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
Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? Chuwei Wang Shanda Li Di He Liwei Wang AI4CE PINN 31 28 0 04 Jun 2022
A deep branching solver for fully nonlinear partial differential equations Jiang Yu Nguwi Guillaume Penent Nicolas Privault 14 14 0 07 Mar 2022
Convergence of a robust deep FBSDE method for stochastic control Kristoffer Andersson Adam Andersson C. Oosterlee 34 19 0 18 Jan 2022
Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems Nikolas Nusken Lorenz Richter PINN DiffM 31 27 0 07 Dec 2021
Computation of conditional expectations with guarantees Patrick Cheridito Balint Gersey 6 2 0 03 Dec 2021
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 8 22 0 07 May 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning Fan Chen J. Huang Chunmei Wang Haizhao Yang 28 30 0 15 Dec 2020
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes Raffaele Marino N. Macris 24 16 0 09 Dec 2020
Convergence of Deep Fictitious Play for Stochastic Differential Games Jiequn Han Ruimeng Hu Jihao Long 19 19 0 12 Aug 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 51 0 09 Jul 2020
Weak error analysis for stochastic gradient descent optimization algorithms A. Bercher Lukas Gonon Arnulf Jentzen Diyora Salimova 28 4 0 03 Jul 2020
Space-time deep neural network approximations for high-dimensional partial differential equations F. Hornung Arnulf Jentzen Diyora Salimova AI4CE 29 19 0 03 Jun 2020
Uniform error estimates for artificial neural network approximations for heat equations Lukas Gonon Philipp Grohs Arnulf Jentzen David Kofler David Siska 29 34 0 20 Nov 2019
Space-time error estimates for deep neural network approximations for differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philipp Zimmermann 29 33 0 11 Aug 2019