Martin Hutzenthaler

Martin Hutzenthaler

Universität Duisburg Essen - Essen / Germany
Natural Sciences / Mathematical Sciences
AD Scientific Index ID: 1783512
University of Duisburg-Essen
person_outline Martin Hutzenthaler's MOST POPULAR ARTICLES
1-) Strong and weak divergence in finite time of Euler\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s method for stochastic differential equations with non-globally Lipschitz continuous coefficientsM Hutzenthaler, A Jentzen, PE KloedenProceedings of the Royal Society A: Mathematical, Physical and Engineering …, 20113872011
2-) Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficientsM Hutzenthaler, A Jentzen, PE KloedenThe Annals of Applied Probability 22 (4), 1611-1641, 20123782012
3-) Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficientsM Hutzenthaler, A JentzenAmerican Mathematical Soc., 20152392015
4-) A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equationsM Hutzenthaler, A Jentzen, T Kruse, TA NguyenSN partial differential equations and applications 1 (2), 10, 20202132020
5-) An overview on deep learning-based approximation methods for partial differential equationsC Beck, M Hutzenthaler, A Jentzen, B KuckuckarXiv preprint arXiv:2012.12348, 20201852020