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Diyora Salimova
Diyora Salimova
University of Freiburg
Verified email at ethz.ch - Homepage
Title
Cited by
Cited by
Year
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
1462021
Deep neural network approximations for Monte Carlo algorithms
P Grohs, A Jentzen, D Salimova
arXiv preprint arXiv:1908.10828, 2019
422019
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
382016
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
312019
Space-time deep neural network approximations for high-dimensional partial differential equations
F Hornung, A Jentzen, D Salimova
arXiv preprint arXiv:2006.02199, 2020
302020
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
292019
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
182022
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
162017
Existence, uniqueness, and numerical approximations for stochastic Burgers equations
S Mazzonetto, D Salimova
Stochastic Analysis and Applications 38 (4), 623-646, 2020
72020
Approximation properties of residual neural networks for Kolmogorov PDEs
J Baggenstos, D Salimova
arXiv preprint arXiv:2111.00215, 2021
62021
Weak error analysis for stochastic gradient descent optimization algorithms
A Bercher, L Gonon, A Jentzen, D Salimova
arXiv preprint arXiv:2007.02723, 2020
22020
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
22018
Numerical approximation results for semilinear parabolic partial differential equations
D Salimova
ETH Zurich, 2019
12019
Predicting the fiber orientation of injection molded components and the geometry influence with neural networks
T Hermann, D Niedziela, D Salimova, T Schweiger
Journal of Composite Materials 58 (15), 1801-1811, 2024
2024
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
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