| 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 |
| Solving high-dimensional optimal stopping problems using deep learning S Becker, P Cheridito, A Jentzen, T Welti European Journal of Applied Mathematics 32 (3), 470-514, 2021 | 111 | 2021 |
| Convergence in Hölder norms with applications to Monte Carlo methods in infinite dimensions S Cox, M Hutzenthaler, A Jentzen, J van Neerven, T Welti IMA Journal of Numerical Analysis 41 (1), 493-548, 2021 | 43 | 2021 |
| Generalised multilevel Picard approximations MB Giles, A Jentzen, T Welti arXiv preprint arXiv:1911.03188, 2019 | 36 | 2019 |
| Strong convergence for explicit space–time discrete numerical approximation methods for stochastic Burgers equations A Jentzen, D Salimova, T Welti Journal of Mathematical Analysis and Applications 469 (2), 661-704, 2019 | 35 | 2019 |
| Weak convergence rates for spatial spectral Galerkin approximations of semilinear stochastic wave equations with multiplicative noise L Jacobe de Naurois, A Jentzen, T Welti Applied Mathematics & Optimization 84 (Suppl 2), 1187-1217, 2021 | 32* | 2021 |
| Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation A Jentzen, T Welti Applied Mathematics and Computation 455, 127907, 2023 | 24 | 2023 |
| Lower bounds for weak approximation errors for spatial spectral Galerkin approximations of stochastic wave equations L Jacobe de Naurois, A Jentzen, T Welti Stochastic Partial Differential Equations and Related Fields: In Honor of …, 2018 | 13 | 2018 |
| On the differentiability of solutions of stochastic evolution equations with respect to their initial values A Andersson, A Jentzen, R Kurniawan, T Welti Nonlinear Analysis 162, 128-161, 2017 | 13 | 2017 |
| High-dimensional stochastic approximation: algorithms and convergence rates T Welti ETH Zurich, 2020 | 3 | 2020 |
| On approximation algorithms for stochastic ordinary differential equations (SDEs) and stochastic partial differential equations (SPDEs) A Jentzen Workshop on Infinite Dimensional Probability, King's College London, 2017 | | 2017 |
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) & University of MünsterBestätigte E-Mail-Adresse bei uni-muenster.de
