| Private Homepage | https://www.uni-muenster.de/AMM/Jentzen/Mitarbeiter/included.shtml |
| Research Interests | Mathematics for machine learning Numerical approximations for high-dimensional partial differential equations Numerical approximations for stochastic differential equations Deep Learning |
| Project membership Mathematics Münster | C: Models and Approximations C1: Evolution and asymptotics C4: Geometry-based modelling, approximation, and reduction |
| Current Publications | • Jentzen A, Wurstemberger P Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates. J. Complexity Vol. 57, 2020, pp 101438 online • Conus D, Jentzen A, Kurniawan R Weak convergence rates of spectral Galerkin approximations for {SPDE}s with nonlinear diffusion coefficients. Ann. Appl. Probab. Vol. 29 (2), 2019, pp 653-716 online • Beck C, E W, Jentzen A Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations. J. Nonlinear Sci. Vol. 29 (4), 2019, pp 1563-1619 online • Da Prato G, Jentzen A, Röckner M A mild Itô formula for {SPDE}s. Trans. Amer. Math. Soc. Vol. 372 (6), 2019, pp 3755-3807 online • Becker S, Cheridito P, Jentzen A Deep optimal stopping. J. Mach. Learn. Res. Vol. 20, 2019, pp Paper No. 74, 25 online • E W, Hutzenthaler M, Jentzen A, Kruse T On multilevel Picard numerical approximations for high-dimensional nonlinear parabolic partial differential equations and high-dimensional nonlinear backward stochastic differential equations. J. Sci. Comput. Vol. 79 (3), 2019, pp 1534-1571 online • Andersson A, Hefter M, Jentzen A, Kurniawan R Regularity properties for solutions of infinite dimensional Kolmogorov equations in Hilbert spaces. Potential Anal. Vol. 50 (3), 2019, pp 347-379 online • Becker S, Jentzen A Strong convergence rates for nonlinearity-truncated Euler-type approximations of stochastic Ginzburg-Landau equations. Stochastic Process. Appl. Vol. 129 (1), 2019, pp 28-69 online • Hefter M, Jentzen A On arbitrarily slow convergence rates for strong numerical approximations of Cox-Ingersoll-Ross processes and squared Bessel processes. Finance Stoch. Vol. 23 (1), 2019, pp 139-172 online |
| Current Projects | • Mathematische Theorie zu Tiefem Lernen online • Existenz-, Eindeutigkeit- und Regularitaetseigenschaften von Loesungen von partielle Differentialgleichungen online • Regularitaetseigeschaften und approximationen fuer stochastische gewoehnliche und partielle Differentialgleichungen mit nicht global Lipschitz-stetigen Nichtlinearitaeten online • Ueberwindung des Fluches der Dimension: Stochastische Approximationsalgorithmen fuer hochdimensionale partielle Differentialgleichungen online • EXC 2044 - C1: Evolution and asymptotics online • EXC 2044 - C3: Interacting particle systems and phase transitions online | ajentzen@uni-muenster.de |
| Phone | +49 251 83-33793 |
| FAX | +49 251 83-32729 |
| Room | 120.005 |
| Secretary | Sekretariat Giesbert Frau Claudia Giesbert Telefon +49 251 83-33792 Fax +49 251 83-32729 Zimmer 120.002 |
| Address | Prof. Dr. Arnulf Jentzen Angewandte Mathematik Münster: Institut für Analysis und Numerik Fachbereich Mathematik und Informatik der Universität Münster Orléans-Ring 10 48149 Münster |
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