Solving the Kolmogorov PDE by means of deep learning

Solving the Kolmogorov PDE by means of deep learning

1 June 2018

Papers citing "Solving the Kolmogorov PDE by means of deep learning"

19 / 19 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
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer Georgios Is. Detorakis 28 0 0 21 Aug 2024
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation Madison Cooley Shandian Zhe Robert M. Kirby Varun Shankar 59 1 0 04 Jun 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
Time integration schemes based on neural networks for solving partial differential equations on coarse grids Xinxin Yan Zhideng Zhou Xiaohan Cheng Xiaolei Yang AI4TS AI4CE 15 0 0 16 Oct 2023
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations Ayoub Farkane Mounir Ghogho M. Oudani M. Boutayeb PINN 25 5 0 07 Apr 2023
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 30 80 0 02 Nov 2022
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning Lorenz Richter Julius Berner 27 19 0 21 Jun 2022
Pricing options on flow forwards by neural networks in Hilbert space F. Benth Nils Detering Luca Galimberti 19 7 0 17 Feb 2022
Convergence of a robust deep FBSDE method for stochastic control Kristoffer Andersson Adam Andersson C. Oosterlee 29 19 0 18 Jan 2022
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 34 6 0 13 Dec 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Arnulf Jentzen Adrian Riekert 27 23 0 09 Jul 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 32 13 0 01 Apr 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
Uniform error estimates for artificial neural network approximations for heat equations Lukas Gonon Philipp Grohs Arnulf Jentzen David Kofler David Siska 26 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 24 33 0 11 Aug 2019
Deep splitting method for parabolic PDEs C. Beck S. Becker Patrick Cheridito Arnulf Jentzen Ariel Neufeld 21 125 0 08 Jul 2019
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philippe von Wurstemberger 11 167 0 07 Sep 2018