Arnulf Jentzen
Title
Cited by
Cited by
Year
Solving high-dimensional partial differential equations using deep learning
J Han, A Jentzen, E Weinan
Proceedings of the National Academy of Sciences 115 (34), 8505-8510, 2018
5342018
Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients
M Hutzenthaler, A Jentzen, PE Kloeden
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2011
3212011
Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients
M Hutzenthaler, A Jentzen, PE Kloeden
Annals of Applied Probability 22 (4), 1611-1641, 2012
3072012
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
W E, J Han, A Jentzen
https://arxiv.org/abs/1706.04702, 2017
277*2017
Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients
M Hutzenthaler, A Jentzen
American Mathematical Society 236 (1112), 2015
1922015
Overcoming the order barrier in the numerical approximation of stochastic partial differential equations with additive space–time noise
A Jentzen, PE Kloeden
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2009
1492009
Taylor approximations for stochastic partial differential equations
A Jentzen, PE Kloeden
Society for Industrial and Applied Mathematics, 2011
1482011
The numerical approximation of stochastic partial differential equations
A Jentzen, PE Kloeden
Milan Journal of Mathematics 77 (1), 205-244, 2009
1432009
Deep optimal stopping
S Becker, P Cheridito, A Jentzen
Journal of Machine Learning Research 20, 74, 2019
1042019
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
P Grohs, F Hornung, A Jentzen, P Von Wurstemberger
arXiv preprint arXiv:1809.02362, 2018
1002018
Loss of regularity for Kolmogorov equations
M Hairer, M Hutzenthaler, A Jentzen
Annals of Probability 43 (2), 468-527, 2015
912015
On a perturbation theory and on strong convergence rates for stochastic ordinary and partial differential equations with non-globally monotone coefficients
M Hutzenthaler, A Jentzen
arXiv preprint arXiv:1401.0295, 2014
87*2014
Divergence of the multilevel Monte Carlo Euler method for nonlinear stochastic differential equations
M Hutzenthaler, A Jentzen, PE Kloeden
Arxiv preprint arXiv:1105.0226, 2011
852011
Galerkin approximations for the stochastic Burgers equation
D Blomker, A Jentzen
SIAM Journal on Numerical Analysis 51 (1), 694-715, 2013
83*2013
Regularity analysis for stochastic partial differential equations with nonlinear multiplicative trace class noise
A Jentzen, M Röckner
arXiv preprint arXiv:1005.4095, 2010
832010
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of …
J Berner, P Grohs, A Jentzen
SIAM Journal on Mathematics of Data Science 2 (3), 631-657, 2020
802020
Efficient simulation of nonlinear parabolic SPDEs with additive noise
A Jentzen, P Kloeden, G Winkel
Annals of Applied Probability 21 (3), 908-950, 2011
792011
A Milstein scheme for SPDEs
A Jentzen, M Röckner
Arxiv preprint arXiv:1001.2751, 2010
79*2010
Solving stochastic differential equations and Kolmogorov equations by means of deep learning
C Beck, S Becker, P Grohs, N Jaafari, A Jentzen
arXiv preprint arXiv:1806.00421, 2018
762018
Pathwise approximation of stochastic differential equations on domains: higher order convergence rates without global Lipschitz coefficients
A Jentzen, PE Kloeden, A Neuenkirch
Numerische Mathematik 112 (1), 41-64, 2009
762009
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Articles 1–20