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1908.00412
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Neural networks-based backward scheme for fully nonlinear PDEs
SN Partial Differential Equations and Applications (SPDEA), 2019
31 July 2019
H. Pham
X. Warin
Maximilien Germain
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Papers citing
"Neural networks-based backward scheme for fully nonlinear PDEs"
23 / 23 papers shown
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Approximation of Solution Operators for High-dimensional PDEs
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From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
Journal of machine learning research (JMLR), 2023
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Leon Sallandt
Nikolas Nusken
271
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28 Jul 2023
A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations
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Nicolas Privault
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28 Sep 2022
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Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?
Neural Information Processing Systems (NeurIPS), 2022
Chuwei Wang
Shanda Li
Di He
Liwei Wang
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04 Jun 2022
A deep branching solver for fully nonlinear partial differential equations
Journal of Computational Physics (JCP), 2022
Jiang Yu Nguwi
Guillaume Penent
Nicolas Privault
228
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07 Mar 2022
An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations
Dan Crisan
Alexander Lobbe
S. Ortiz-Latorre
186
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10 Jan 2022
A novel control method for solving high-dimensional Hamiltonian systems through deep neural networks
SIAM Journal on Scientific Computing (SISC), 2021
Shaolin Ji
S. Peng
Ying Peng
Xichuan Zhang
195
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04 Nov 2021
Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics
R. Frey
Verena Köck
169
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23 Sep 2021
Cell-average based neural network method for hyperbolic and parabolic partial differential equations
Changxin Qiu
Jue Yan
181
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02 Jul 2021
Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs
MCSS. Mathematics of Control, Signals and Systems (MCSS), 2021
Jérome Darbon
P. Dower
Tingwei Meng
333
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07 May 2021
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
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22 Dec 2020
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes
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09 Dec 2020
Actor-Critic Algorithm for High-dimensional Partial Differential Equations
Xiaohan Zhang
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07 Oct 2020
Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations
Lorenc Kapllani
Long Teng
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03 Oct 2020
Convergence of Deep Fictitious Play for Stochastic Differential Games
Jiequn Han
Ruimeng Hu
Jihao Long
438
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12 Aug 2020
Space-time deep neural network approximations for high-dimensional partial differential equations
F. Hornung
Arnulf Jentzen
Diyora Salimova
AI4CE
260
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03 Jun 2020
Uniform error estimates for artificial neural network approximations for heat equations
IMA Journal of Numerical Analysis (IMA J. Numer. Anal.), 2019
Lukas Gonon
Philipp Grohs
Arnulf Jentzen
David Kofler
David Siska
348
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20 Nov 2019
Deep neural network approximations for Monte Carlo algorithms
Philipp Grohs
Arnulf Jentzen
Diyora Salimova
242
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28 Aug 2019
Space-time error estimates for deep neural network approximations for differential equations
Advances in Computational Mathematics (Adv. Comput. Math.), 2019
Philipp Grohs
F. Hornung
Arnulf Jentzen
Philipp Zimmermann
238
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11 Aug 2019
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
C. Reisinger
Yufei Zhang
193
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15 Mar 2019
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