Projects
Hessian scientists of various disciplines are using High Performance Computers for their research.
Projects
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Projekte
Displaying 1 - 20 of 20
Structure Optimization, Dielectric and Magnetic Properties of Pure Silicon as well as Iron-Doped Silicon and Tin Clusters
The previous investigation of SnN clusters with N = 6−40 showed that below a clusters size of about 30 atoms, a prolate ...
Natural SciencesPhysical and Theoretical Chemistry
Amortized Variational Inference by Policy Search
Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...
Engineering SciencesComputer Science
CYWARN – Entwicklung von Strategien und Technologien zur Analyse und Kommunikation der Sicherheitslage im Cyberraum II
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Engineering SciencesComputer Science
Detecting Suitcase Damages With Artificial Intelligence – IT-Project Lufthansa
Artificial intelligence is currently developing faster than ever and introduces many different possibilities. Our client ...
Engineering SciencesComputer Science-
Gradient-Based Variational Inference by Policy Search
Many problems in machine learning involve inference from intractable distributions. For example, when learning latent ...
Engineering SciencesComputer Science 
Imitation Learning for Lap Time Simulation
In order to facilitate rapid prototyping and testing in the advanced motorsport industry, we consider the problem of ...
Engineering SciencesComputer Science
Investigation of Spin Isomers in SnNFe with N = 8-16
In the framework of the Collaborative Research Center (CRC 1487), iron is studied as a substitute for rare-earth metals ...
Natural SciencesPhysical and Theoretical Chemistry
Structure Discrimination and Magnetic Properties of FeSnN with N =8–15
In the framework of the Collaborative Research Center (CRC 1487), iron is studied as a substitute for rare-earth metals ...
Natural SciencesPhysical and Theoretical Chemistry
Understanding and Exploiting an Iron Catalysed Route to New Chemical Synthons II
The project focuses on an iron catalysed method to prepare unprecedented chemical synthons, developed in the group of Dr ...
Natural SciencesPhysical and Theoretical Chemistry
Combining Regenerative and Degenerative Methods in Growing Neural Networks
Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...
Engineering SciencesComputer Science
CYWARN – Entwicklung von Strategien und Technologien zur Analyse und Kommunikation der Sicherheitslage im Cyberraum
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Engineering SciencesComputer Science
Optical Spectroscopy, Dielectric Properties and Structure Discrimination of SnN (20 ≤ N ≤ 40)
The previous investigation of neutral SnN clusters with N = 6 - 20 showed that for clusters with N ≤ 15 prolate ...
Natural SciencesPhysical and Theoretical Chemistry
Structure Discrimination of SnNGa with N = 9–15
Since semiconductors are fundamental for modern technologies, the research is focused on such materials and their ...
Natural SciencesPhysical and Theoretical Chemistry
Understanding and Exploiting an Iron Catalysed Route to New Chemical Synthons
The project focuses on an iron catalysed method to prepare unprecedented chemical synthons, developed in the group of Dr ...
Natural SciencesPhysical and Theoretical Chemistry-
Noise Resilient Empirical Performance Modeling with Deep Neural Networks
Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set ...
Engineering SciencesComputer Science 
Optical Spectroscopy, Dielectric Properties and Structure Discrimination of Sn+N (20≤N≤40) Clusters
Semiconductor materials are of great importance in the development of technological devices. In particular, the trend ...
Natural SciencesPhysical and Theoretical Chemistry-
Model Learning With Recurrent Kalman Networks
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...
Engineering SciencesComputer Science 
Network Traffic Prediction with Neural Networks
The bursty nature of network traffic is one of the main reasons for congestion in data centers, since traffic loads are ...
Engineering SciencesComputer Science-
Performance Modeling at a Discount
Identifying scalability bugs in parallel applications is a vital but also laborious and expensive task. Empirical ...
Engineering SciencesComputer Science -
Stochastic Search for Applications in Robotics
Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...
Engineering SciencesComputer Science
