Martin Hutzenthaler
Orcid: 0000-0003-0738-8717
According to our database1,
Martin Hutzenthaler
authored at least 20 papers
between 2011 and 2023.
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Bibliography
2023
Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations.
J. Num. Math., 2023
2022
Strong convergence rate of Euler-Maruyama approximations in temporal-spatial Hölder-norms.
J. Comput. Appl. Math., 2022
Overcoming the Curse of Dimensionality in the Numerical Approximation of Parabolic Partial Differential Equations with Gradient-Dependent Nonlinearities.
Found. Comput. Math., 2022
Deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear partial differential equations.
CoRR, 2022
Multilevel Picard approximations for high-dimensional decoupled forward-backward stochastic differential equations.
CoRR, 2022
Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions.
CoRR, 2022
2021
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions.
CoRR, 2021
Strong L<sup>p</sup>-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations.
CoRR, 2021
Multilevel Picard approximations for McKean-Vlasov stochastic differential equations.
CoRR, 2021
Full history recursive multilevel Picard approximations for ordinary differential equations with expectations.
CoRR, 2021
2020
Multilevel Picard Approximations of High-Dimensional Semilinear Parabolic Differential Equations with Gradient-Dependent Nonlinearities.
SIAM J. Numer. Anal., 2020
Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations.
J. Num. Math., 2020
An overview on deep learning-based approximation methods for partial differential equations.
CoRR, 2020
Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities.
CoRR, 2020
Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations.
CoRR, 2020
2019
On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations.
J. Sci. Comput., 2019
Strong convergence rates on the whole probability space for space-time discrete numerical approximation schemes for stochastic Burgers equations.
CoRR, 2019
2018
Exponential integrability properties of numerical approximation processes for nonlinear stochastic differential equations.
Math. Comput., 2018
2011
Found. Comput. Math., 2011