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
115 2021 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
44 2022 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
35 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
28 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
28 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
22 2020 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
15 2017 Existence, uniqueness, and numerical approximations for stochastic Burgers equations S Mazzonetto, D Salimova
Stochastic Analysis and Applications 38 (4), 623-646, 2020
6 2020 Approximation properties of residual neural networks for Kolmogorov PDEs J Baggenstos, D Salimova
arXiv preprint arXiv:2111.00215, 2021
2 2021 Weak error analysis for stochastic gradient descent optimization algorithms A Bercher, L Gonon, A Jentzen, D Salimova
arXiv preprint arXiv:2007.02723, 2020
2 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 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, G Da Prato, E Weinan, M Gerencsér, M Hairer, M Hefter, ...
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 On an inequality in ZO Ibragimov, DF Salimova
Elemente der Mathematik 70 (2), 79-81, 2015
2015 
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen & University of MünsterBestätigte E-Mail-Adresse bei uni-muenster.de
