| Solving high-dimensional partial differential equations using deep learning J Han, A Jentzen, W E Proceedings of the National Academy of Sciences 115 (34), 8505-8510, 2018 | 2085 | 2018 |
| 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 | 813* | 2017 |
| 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 | 548 | 2011 |
| Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients M Hutzenthaler, A Jentzen, PE Kloeden | 523 | 2012 |
| Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients M Hutzenthaler, A Jentzen American Mathematical Society 236 (1112), 2015 | 298 | 2015 |
| 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 Memoirs of the American Mathematical Society 284, 1-106, 2023 | 291 | 2023 |
| Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations C Beck, W E, A Jentzen Journal of Nonlinear Science 29, 1563-1619, 2019 | 287 | 2019 |
| Deep optimal stopping S Becker, P Cheridito, A Jentzen Journal of Machine Learning Research 20 (74), 1-25, 2019 | 274 | 2019 |
| Solving the Kolmogorov PDE by means of deep learning C Beck, S Becker, P Grohs, N Jaafari, A Jentzen Journal of Scientific Computing 88, 1-28, 2021 | 238 | 2021 |
| 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 | 226 | 2020 |
| 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), 10, 2020 | 222 | 2020 |
| Taylor approximations for stochastic partial differential equations A Jentzen, PE Kloeden Society for Industrial and Applied Mathematics, 2011 | 202 | 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 | 196 | 2009 |
| An overview on deep learning-based approximation methods for partial differential equations C Beck, M Hutzenthaler, A Jentzen, B Kuckuck arXiv preprint arXiv:2012.12348, 2020 | 192 | 2020 |
| The numerical approximation of stochastic partial differential equations A Jentzen, PE Kloeden Milan Journal of Mathematics 77, 205-244, 2009 | 192 | 2009 |
| Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning E Weinan, J Han, A Jentzen Nonlinearity 35 (1), 278, 2021 | 180 | 2021 |
| Deep splitting method for parabolic PDEs C Beck, S Becker, P Cheridito, A Jentzen, A Neufeld SIAM Journal on Scientific Computing 43 (5), A3135-A3154, 2021 | 180 | 2021 |
| A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant … A Jentzen, D Salimova, T Welti arXiv preprint arXiv:1809.07321, 2018 | 171 | 2018 |
| 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 | 159 | 2020 |
| DNN expression rate analysis of high-dimensional PDEs: application to option pricing D Elbrächter, P Grohs, A Jentzen, C Schwab Constructive Approximation 55 (1), 3-71, 2022 | 148 | 2022 |
Martin HutzenthalerUniversity of Duisburg-Essen, MathematicsEmail verificata su uni-due.de
