Machine Learning for semi linear PDEs

Machine Learning for semi linear PDEs

20 September 2018

Quentin Chan-Wai-Nam

Papers citing "Machine Learning for semi linear PDEs"

19 / 19 papers shown

Title
Integration Matters for Learning PDEs with Backwards SDEs Sungje Park Stephen Tu PINN 53 0 0 02 May 2025
Solution of FPK Equation for Stochastic Dynamics Subjected to Additive Gaussian Noise via Deep Learning Approach Amir H. Khodabakhsh S. Pourtakdoust 13 6 0 08 Nov 2023
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations Ayoub Farkane Mounir Ghogho M. Oudani M. Boutayeb PINN 20 5 0 07 Apr 2023
Simultaneous upper and lower bounds of American option prices with hedging via neural networks Ivan Guo Nicolas Langrené Jiahao Wu 17 0 0 24 Feb 2023
Physics informed WNO N. N. Tapas Tripura S. Chakraborty 25 28 0 12 Feb 2023
Mean-field neural networks: learning mappings on Wasserstein space H. Pham X. Warin 21 13 0 27 Oct 2022
A Deep Learning Approach for the solution of Probability Density Evolution of Stochastic Systems S. Pourtakdoust Amir H. Khodabakhsh 30 12 0 05 Jul 2022
Convergence of a robust deep FBSDE method for stochastic control Kristoffer Andersson Adam Andersson C. Oosterlee 27 19 0 18 Jan 2022
Cell-average based neural network method for hyperbolic and parabolic partial differential equations Changxin Qiu Jue Yan 14 10 0 02 Jul 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
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes Raffaele Marino N. Macris 12 16 0 09 Dec 2020
Space-time deep neural network approximations for high-dimensional partial differential equations F. Hornung Arnulf Jentzen Diyora Salimova AI4CE 14 19 0 03 Jun 2020
Differential Machine Learning B. Huge Antoine Savine 13 32 0 05 May 2020
Uniform error estimates for artificial neural network approximations for heat equations Lukas Gonon Philipp Grohs Arnulf Jentzen David Kofler David Siska 13 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 19 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
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications Achref Bachouch Côme Huré N. Langrené H. Pham 8 84 0 13 Dec 2018
Unbiased deep solvers for linear parametric PDEs Marc Sabate Vidales David Siska Lukasz Szpruch OOD 24 7 0 11 Oct 2018