| 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 Communications in Mathematical Sciences 19 (5), 1167-1205, 2021 | 164 | 2021 |
| Deep neural network approximations for Monte Carlo algorithms P Grohs, A Jentzen, D Salimova arXiv preprint arXiv:1908.10828, 2019 | 42 | 2019 |
| Strong convergence of full-discrete nonlinearity-truncated accelerated exponential Euler-type approximations for stochastic Kuramoto-Sivashinsky equations M Hutzenthaler, A Jentzen, D Salimova arXiv preprint arXiv:1604.02053, 2016 | 41 | 2016 |
| Strong and weak divergence of exponential and linear-implicit Euler approximations for stochastic partial differential equations with superlinearly growing nonlinearities M Beccari, M Hutzenthaler, A Jentzen, R Kurniawan, F Lindner, ... arXiv preprint arXiv:1903.06066, 2019 | 33 | 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 | 33 | 2019 |
| Space-time deep neural network approximations for high-dimensional partial differential equations F Hornung, A Jentzen, D Salimova arXiv preprint arXiv:2006.02199, 2020 | 30 | 2020 |
| Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms P Grohs, A Jentzen, D Salimova Partial Differential Equations and Applications 3 (4), 45, 2022 | 23 | 2022 |
| On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions M Gerencsér, A Jentzen, D Salimova Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 16 | 2017 |
| Existence, uniqueness, and numerical approximations for stochastic Burgers equations S Mazzonetto, D Salimova Stochastic Analysis and Applications 38 (4), 623-646, 2020 | 8 | 2020 |
| Approximation properties of residual neural networks for Kolmogorov PDEs J Baggenstos, D Salimova arXiv preprint arXiv:2111.00215, 2021 | 7 | 2021 |
| Weak error analysis for stochastic gradient descent optimization algorithms A Bercher, L Gonon, A Jentzen, D Salimova arXiv preprint arXiv:2007.02723, 2020 | 4 | 2020 |
| Existence and uniqueness properties for solutions of a class of Banach space valued evolution equations A Jentzen, S Mazzonetto, D Salimova arXiv preprint arXiv:1812.06859, 2018 | 2 | 2018 |
| Predicting the fiber orientation of injection molded components and the geometry influence with neural networks T Herrmann, D Niedziela, D Salimova, T Schweiger Journal of Composite Materials 58 (15), 1801-1811, 2024 | 1 | 2024 |
| Numerical approximation results for semilinear parabolic partial differential equations D Salimova ETH Zurich, 2019 | 1 | 2019 |
| On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions D Salimova 2017 Foundations of Computational Mathematics: Book of Abstracts, 90-90, 2017 | | 2017 |
| Efficient stochastic numerical approximation algorithms for high-dimensional nonlinear PDEs A Jentzen, E Weinan, M Gerencsér, M Hairer, M Hefter, M Hutzenthaler, ... Conference on Nonlinear PDEs, Stochastic Control and Filtering, ICMS, 2017 | | 2017 |
| On numerical approximation algorithms for high-dimensional nonlinear PDEs, nonlinear SDEs, and high-dimensional nonlinear FBSDEs A Jentzen, E Weinan, M Gerencsér, M Hairer, M Hefter, M Hutzenthaler, ... KI-Net Conference: Selected topics in transport phenomena: deterministic and …, 2017 | | 2017 |
| 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 |
| On stochastic numerical methods for the approximative pricing of financial derivatives A Jentzen, E Weinan, M Gerencsér, M Hairer, M Hefter, M Hutzenthaler, ... Workshop on multiscale methods for stochastic dynamics, 2017 | | 2017 |
| Numerical approximations for stochastic ordinary and partial differential equations A Jentzen School of Stochastic Dynamical Systems and Ergodicity, 2016 | | 2016 |
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen & University of MünsterBestätigte E-Mail-Adresse bei uni-muenster.de
