
Projects
Hessian scientists of various disciplines are using High Performance Computers for their research.
Hessian scientists of various disciplines are using High Performance Computers for their research.
Displaying 1 - 21 of 21
The objective of this ongoing project is the continuous development and advancement of effective simulation methods for ...
In the research area of circular building design, design concepts for sustainable masonry are being implemented using ...
Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Artificial intelligence is currently developing faster than ever and introduces many different possibilities. Our client ...
Many problems in machine learning involve inference from intractable distributions. For example, when learning latent ...
In order to facilitate rapid prototyping and testing in the advanced motorsport industry, we consider the problem of ...
Over the past decade, there has been a significant shift in the industrial sector towards sustainability, leading to ...
Recent work has shown that deep neural networks are able to predict human similarity judgments with high accuracy (e.g ...
Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Permanent magnets are the key component of electric motors and generators, which currently have a high demand in the ...
In 2018, Gottschall et al. proposed a multi-stimulit concept for magnetocaloric cooling. [1] The energy consumption ...
Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set ...
The crustal stress state is an important information for many geological applications, e.g. directional drilling ...
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...
The bursty nature of network traffic is one of the main reasons for congestion in data centers, since traffic loads are ...
Identifying scalability bugs in parallel applications is a vital but also laborious and expensive task. Empirical ...
Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...
Brake discs are put to high thermomechanical stresses during operation. These loads might induce the so called heat ...
Magnetic materials have been widely used in the modern society due to their intriguing functional properties. For ...