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On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations.

W. E, M. Hutzenthaler, A. Jentzen, and T. Kruse. J. Sci. Comput., 79 (3): 1534-1571 (2019)

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Martin Hutzenthaler

Alexandra Elisabeth Hutzenthaler

Martin Brückner

Vergleichende Untersuchungen zur Inzidenz apoptotischer und proliferativer Veränderungen kultivierter, humaner Knochenzellen von Ober- und Unterkiefer auf maschinenbearbeitetem, reinem Titan über 25 TageM. Brückner. Uni Leipzig, (2009)

Martin Breitenfeldt

Mass measurements on short-lived Cd and Ag nuclides at the online mass spectrometer ISOLTRAPM. Breitenfeldt. Uni Greifswald, (2009)

Martin Faes

Analyse der mehrskaligen Transportmechanismen beim laminaren Mischen mittels laseroptischer und numerischer MethodenM. Faes. Uni Hannover, (2009)

Other publications of authors with the same name

Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations.C. Beck, F. Hornung, M. Hutzenthaler, A. Jentzen, and T. Kruse. J. Num. Math., 28 (4): 197-222 (2020)Full history recursive multilevel Picard approximations for ordinary differential equations with expectations.C. Beck, M. Hutzenthaler, A. Jentzen, and E. Magnani. CoRR, (2021)On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations.W. E, M. Hutzenthaler, A. Jentzen, and T. Kruse. J. Sci. Comput., 79 (3): 1534-1571 (2019)Multilevel Picard approximations for McKean-Vlasov stochastic differential equations.M. Hutzenthaler, T. Kruse, and T. Nguyen. CoRR, (2021)Deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear partial differential equations.P. Cioica-Licht, M. Hutzenthaler, and P. Werner. CoRR, (2022)An overview on deep learning-based approximation methods for partial differential equations.C. Beck, M. Hutzenthaler, A. Jentzen, and B. Kuckuck. CoRR, (2020)Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities.M. Hutzenthaler, A. Jentzen, T. Kruse, and T. Nguyen. CoRR, (2020)Strong convergence rates on the whole probability space for space-time discrete numerical approximation schemes for stochastic Burgers equations.M. Hutzenthaler, A. Jentzen, F. Lindner, and P. Pusnik. CoRR, (2019)Strong Lp-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations.M. Hutzenthaler, A. Jentzen, B. Kuckuck, and J. Padgett. CoRR, (2021)Overcoming the Curse of Dimensionality in the Numerical Approximation of Parabolic Partial Differential Equations with Gradient-Dependent Nonlinearities.M. Hutzenthaler, A. Jentzen, and T. Kruse. Found. Comput. Math., 22 (4): 905-966 (2022)