activity
20202025
most citedA proof of convergence for gradient descent in the training of artificial neural networks for constant target functions

26 citations · 96 across the 22 of their papers we have counts for

collaborators
Showing 2023 · math.NAShow all

2 papers · 2 filters

math.NA2023

Deep neural network approximation of composite functions without the curse of dimensionality

Adrian Riekert

In this article we identify a general class of high-dimensional continuous functions that can be approximated by deep neural networks (DNNs) with the rectified linear unit (ReLU) a…

math.NA2023

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations

Arnulf Jentzen, Adrian Riekert, Philippe von Wurstemberger

In this article we propose a new deep learning approach to approximate operators related to parametric partial differential equations (PDEs). In particular, we introduce a new stra…