Dr. Benno Kuckuck

Current affiliations

Professorship for applied mathematics (Prof. Jentzen)

Publications

An overview on deep learning-based approximation methods for partial differential equations Beck C, Hutzenthaler M, Jentzen A, Kuckuck B (2023) In: Discrete and Continuous Dynamical Systems - Series B, 28(6), 3697-3746. doi:10.3934/dcdsb.2022238 Research article (journal) \| Peer reviewed \| Published
Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions Boussange, V.; Becker, S.; Jentzen, A.; Kuckuck, B.; Pellissier, L. (2023) In: SN Partial Differential Equations and Applications, 4, 51. doi:10.1007/s42985-023-00244-0 Research article (journal) \| Peer reviewed \| Published
Counterexamples to local Lipschitz and local Hölder continuity with respect to the initial values for additive noise driven SDEs with smooth drift coefficient functions with at most polynomially growing derivatives Jentzen A, Kuckuck B, Müller-Gronbach T, Yaroslavtseva L (2022) In: Discrete and Continuous Dynamical Systems - Series B, 27(7), 3707-3724. doi:10.3934/dcdsb.2021203 Research article (journal) \| Peer reviewed \| Published
Full error analysis for the training of deep neural networks Beck C, Jentzen A, Kuckuck B (2022) In: Infinite Dimensional Analysis, Quantum Probability and Related Topics, 25(2), 2150020. doi:10.1142/S021902572150020X Research article (journal) \| Peer reviewed \| Published
Strong error analysis for stochastic gradient descent optimization algorithms Jentzen A, Kuckuck B, Neufeld A, von Wurstemberger P (2021) In: IMA Journal of Numerical Analysis, 41(1), 455-492. doi:10.1093/imanum/drz055 Research article (journal) \| Peer reviewed \| Published

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Projects

Mathematical Theory for Deep Learning (DLMATH)

Duration: 01/09/2019 - 31/08/2024

Type of project: Own resources project