Private Homepagehttps://www.uni-muenster.de/AMM/Jentzen/Mitarbeiter/included.shtml
Research InterestsMathematics 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 PublicationsJentzen 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
E-Mailajentzen@uni-muenster.de
Phone+49 251 83-33793
FAX+49 251 83-32729
Room120.005
Secretary   Sekretariat Giesbert
Frau Claudia Giesbert
Telefon +49 251 83-33792
Fax +49 251 83-32729
Zimmer 120.002

AddressProf. 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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