| 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 | 446* | 2018 |
| 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 | 314 | 2011 |
| 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 | 296 | 2012 |
| 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 | 247* | 2017 |
| Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients M Hutzenthaler, A Jentzen American Mathematical Society 236 (1112), 2015 | 189 | 2015 |
| Taylor approximations for stochastic partial differential equations A Jentzen, PE Kloeden Society for Industrial and Applied Mathematics, 2011 | 147 | 2011 |
| 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 | 146 | 2009 |
| The numerical approximation of stochastic partial differential equations A Jentzen, PE Kloeden Milan Journal of Mathematics 77 (1), 205-244, 2009 | 138 | 2009 |
| Deep optimal stopping S Becker, P Cheridito, A Jentzen Journal of Machine Learning Research 20, 74, 2019 | 92 | 2019 |
| 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 | 88 | 2018 |
| Loss of regularity for Kolmogorov equations M Hairer, M Hutzenthaler, A Jentzen Annals of Probability 43 (2), 468-527, 2015 | 86 | 2015 |
| 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 | 85 | 2011 |
| 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 | 82* | 2014 |
| Regularity analysis for stochastic partial differential equations with nonlinear multiplicative trace class noise A Jentzen, M Röckner arXiv preprint arXiv:1005.4095, 2010 | 81 | 2010 |
| Galerkin approximations for the stochastic Burgers equation D Blomker, A Jentzen SIAM Journal on Numerical Analysis 51 (1), 694-715, 2013 | 80* | 2013 |
| Efficient simulation of nonlinear parabolic SPDEs with additive noise A Jentzen, P Kloeden, G Winkel Annals of Applied Probability 21 (3), 908-950, 2011 | 77 | 2011 |
| 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 | 76 | 2009 |
| Pathwise numerical approximations of SPDEs with additive noise under non-global Lipschitz coefficients A Jentzen Potential Analysis 31 (4), 375, 2009 | 74 | 2009 |
| A Milstein scheme for SPDEs A Jentzen, M Röckner Arxiv preprint arXiv:1001.2751, 2010 | 73* | 2010 |
| 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 | 70 | 2020 |
Martin HutzenthalerUniversity of Duisburg-Essen, MathematicsBestätigte E-Mail-Adresse bei uni-due.de
