| 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 | 661 | 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 | 339 | 2011 |
| Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients M Hutzenthaler, A Jentzen, PE Kloeden The Annals of Applied Probability 22 (4), 1611-1641, 2012 | 329 | 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 | 322* | 2017 |
| Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients M Hutzenthaler, A Jentzen American Mathematical Soc., 2015 | 205 | 2015 |
| 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 | 157 | 2009 |
| Taylor approximations for stochastic partial differential equations A Jentzen, PE Kloeden Society for Industrial and Applied Mathematics, 2011 | 152 | 2011 |
| The numerical approximation of stochastic partial differential equations A Jentzen, PE Kloeden Milan Journal of Mathematics 77 (1), 205-244, 2009 | 148 | 2009 |
| 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 | 124 | 2018 |
| Deep optimal stopping S Becker, P Cheridito, A Jentzen Journal of Machine Learning Research 20, 74, 2019 | 112 | 2019 |
| 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 | 95 | 2020 |
| On a perturbation theory and on strong convergence rates for stochastic ordinary and partial differential equations with nonglobally monotone coefficients M Hutzenthaler, A Jentzen The Annals of Probability 48 (1), 53-93, 2020 | 94 | 2020 |
| Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations C Beck, E Weinan, A Jentzen Journal of Nonlinear Science 29 (4), 1563-1619, 2019 | 94 | 2019 |
| Loss of regularity for Kolmogorov equations M Hairer, M Hutzenthaler, A Jentzen The Annals of Probability 43 (2), 468-527, 2015 | 93 | 2015 |
| 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 | 91 | 2018 |
| 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 | 88 | 2011 |
| Galerkin approximations for the stochastic Burgers equation D Blomker, A Jentzen SIAM Journal on Numerical Analysis 51 (1), 694-715, 2013 | 86* | 2013 |
| Regularity analysis for stochastic partial differential equations with nonlinear multiplicative trace class noise A Jentzen, M Röckner arXiv preprint arXiv:1005.4095, 2010 | 86 | 2010 |
| A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations M Hutzenthaler, A Jentzen, T Kruse, TA Nguyen SN partial differential equations and applications 1 (2), 1-34, 2020 | 83 | 2020 |
| Efficient simulation of nonlinear parabolic SPDEs with additive noise A Jentzen, P Kloeden, G Winkel The Annals of Applied Probability 21 (3), 908-950, 2011 | 80 | 2011 |
Martin HutzenthalerUniversity of Duisburg-Essen, MathematicsBestätigte E-Mail-Adresse bei uni-due.de
