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Deep learning based numerical approximation algorithms for stochastic partial differential equations
v1v2 (latest)

Deep learning based numerical approximation algorithms for stochastic partial differential equations

2 December 2020
C. Beck
S. Becker
Patrick Cheridito
Arnulf Jentzen
Ariel Neufeld
ArXiv (abs)PDFHTMLGithub (5★)

Papers citing "Deep learning based numerical approximation algorithms for stochastic partial differential equations"

9 / 9 papers shown
High-dimensional Bayesian filtering through deep density approximation
High-dimensional Bayesian filtering through deep density approximation
Kasper Bågmark
Filip Rydin
105
0
0
10 Nov 2025
Nonlinear filtering based on density approximation and deep BSDE prediction
Nonlinear filtering based on density approximation and deep BSDE prediction
Kasper Bågmark
Adam Andersson
S. Larsson
116
1
0
14 Aug 2025
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in LpL^pLp-sense
Ariel Neufeld
Tuan Anh Nguyen
369
1
0
30 Sep 2024
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEsCommunications in nonlinear science & numerical simulation (CNSNS), 2024
Ariel Neufeld
Philipp Schmocker
Sizhou Wu
638
13
0
08 May 2024
An efficient Monte Carlo scheme for Zakai equations
An efficient Monte Carlo scheme for Zakai equationsCommunications in nonlinear science & numerical simulation (CNSNS), 2022
C. Beck
S. Becker
Patrick Cheridito
Arnulf Jentzen
Ariel Neufeld
149
2
0
24 Oct 2022
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Luca Galimberti
Anastasis Kratsios
Giulia Livieri
OOD
482
19
0
24 Oct 2022
Computation of conditional expectations with guarantees
Computation of conditional expectations with guarantees
Patrick Cheridito
Balint Gersey
319
5
0
03 Dec 2021
Differentiable Physics: A Position Piece
Differentiable Physics: A Position Piece
Bharath Ramsundar
Dilip Krishnamurthy
V. Viswanathan
PINNAI4CE
285
18
0
14 Sep 2021
An overview on deep learning-based approximation methods for partial
  differential equations
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
673
175
0
22 Dec 2020
1
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