Benno Kuckuck

activity

20112026

most citedAn overview on deep learning-based approximation methods for partial differential equations

88 citations · 204 across the 14 of their papers we have counts for

collaborators

Showing 2021Show all

2 papers · 1 filter

math.NA2021★ 4 cited

Deep neural network approximation theory for high-dimensional functions

Pierfrancesco Beneventano, Patrick Cheridito, Robin Graeber +2

The purpose of this article is to develop a machinery to study the capacity of deep neural networks (DNNs) to approximate high-dimensional functions. In particular, we show that DN…

math.NA2021

Strong $L^{p}$ -error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations

Martin Hutzenthaler, Arnulf Jentzen, Benno Kuckuck +1

Full-history recursive multilevel Picard (MLP) approximation schemes have been shown to overcome the curse of dimensionality in the numerical approximation of high-dimensional semi…

Deep neural network approximation theory for high-dimensional functions

Strong Lp-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations

Strong $L^{p}$ -error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations