Timo Welti
Timo Welti
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Zitiert von
Zitiert von
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
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
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
Generalised multilevel Picard approximations
MB Giles, A Jentzen, T Welti
arXiv preprint arXiv:1911.03188, 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
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
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
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
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
High-dimensional stochastic approximation: algorithms and convergence rates
T Welti
ETH Zurich, 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
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