43 citations · 49 across the 5 of their papers we have counts for
5 papers
Efficient and Scalable Kernel Matrix Approximations using Hierarchical Decomposition
Keerthi Gaddameedi, Severin Reiz, Tobias Neckel +1
With the emergence of Artificial Intelligence, numerical algorithms are moving towards more approximate approaches. For methods such as PCA or diffusion maps, it is necessary to co…
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty
Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl +3
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extende…
Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs
Severin Reiz, Tobias Neckel, Hans-Joachim Bungartz
Training deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed.…
Enabling Radiative Transfer on AMR grids in CRASH
N. Hariharan, L. Graziani, B. Ciardi +2
We introduce CRASH-AMR, a new version of the cosmological Radiative Transfer (RT) code CRASH, enabled to use refined grids. This new feature allows us to attain higher resolution i…
ls1 mardyn: The massively parallel molecular dynamics code for large systems
Christoph Niethammer, Stefan Becker, Martin Bernreuther +9
The molecular dynamics simulation code ls1 mardyn is presented. It is a highly scalable code, optimized for massively parallel execution on supercomputing architectures, and curren…