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). Research article (journal)
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. Research article (journal)
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). Research article (journal)
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. Research article (journal)
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). Research article (journal)

EXC 2044 - T10: Deep learning and surrogate methods Duration: 01/01/2026 - 31/12/2032 \| 1st Funding period Funded by: DFG - Cluster of Excellence Type of project: Subproject in DFG-joint project hosted at University of Münster
Mathematical Theory for Deep Learning (DLMATH) Duration: 01/09/2019 - 31/08/2024 Type of project: Own resources project