
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 - 30 of 33
The computational resources of the Lichtenberg HPC were used in this project to train physics informed machine learning ...
The project seeks to create a comprehensive model linking nanoscale phenomena to larger-scale behaviors to better ...
Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...
During development and adult homeostasis, cells in our bodies need to constantly integrate internal and external ...
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 ...
Vibroacoustic vehicle behavior specifies among others the quality of vehicles and contributes to customer satisfaction ...
The project aims to develop a bridging model that connects the nanoscale to the upscaled levels for understanding the ...
Detailed multi-scale modelling provides in-depth insights into the complex phenomena of catalytic systems that typically ...
Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...
During development and adult homeostasis, cells in our bodies need to constantly integrate internal and external ...
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
This project was a follow-up project for our initial project intended at setting up Lichtenberg Cluster for the use ...
The prediction accuracy of models is affected using deterministic parameters. That is because the uncertainty of these ...
Overall objective of this project is to develop an elementary physical/chemical bridging model for the chemical reaction ...
This project was a follow-up project for our initial project intended at setting up Lichtenberg Cluster for the use ...
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 ...
To model future energy systems and their markets, it is important to understand the underlying dynamics of their ...
Understanding the behavior of different materials not only furthers general knowledge, but can also often be used for ...
Lighting, both natural and artificial, has become a part of our daily lives that have been taken for granted in modern ...
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...
In need for alternatives to conventional cooling devices, the magnetocaloric effect (MCE) is one of the most promising ...
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 ...
The electromechanical coupling properties of ferroelectric materials are widely studied and used to design macro and ...
Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...
Future wireless communication systems are envisioned to meet stringent latency requirements to support control ...