
Projekte
Hessische Wissenschaftlerinnen und Wissenschaftler unterschiedlichster Disziplinen benötigen Hochleistungsrechnen für ihre Forschung.
Hessische Wissenschaftlerinnen und Wissenschaftler unterschiedlichster Disziplinen benötigen Hochleistungsrechnen für ihre Forschung.
Displaying 1 - 30 of 33
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
Understanding the underlying reward mechanisms of human locomotion and transferring this knowledge to humanoid robots is ...
Visuotactile sensors are gaining momentum in robotics because they provide high-resolution contact measurements at a ...
Reinforcement Learning (RL) is a promising tool for solving complicated control and decision-making tasks in a data ...
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning ...
Autonomous robotic assembly requires a well-orchestrated sequence of high-level actions and smooth manipulation ...
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 wireless communications, beamforming is utilized for the reception and transmission of directional wireless signals ...
Transcription is the process of converting DNA into RNA and is essential to cellular life. The transcription process ...
Deep Learning is the major component of the success of most new Artificial Intelligence applications. A new promising ...
Within the last decade, deep neural networks have attracted much attention from academia and industry. Such a wide ...
Normative computational models of human sensorimotor behavior based on optimal feedback control with signal-dependent ...
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy ...
In 2018, Gottschall et al. proposed a multi-stimulit concept for magnetocaloric cooling. [1] The energy consumption ...
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 ...
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the ...
The Nonparametric Off-Policy Policy Gradient (NOPG) is a policy gradient algorithm to solve reinforcement learning tasks ...
The aim of the project is to develop a new family of optimization algorithms to handle convex constraints and evaluate ...
Precise models of the system dynamics are crucial for model-based control and reinforcement learning (RL) in autonomous ...
Lighting, both natural and artificial, has become a part of our daily lives that have been taken for granted in modern ...
Deep Reinforcement Learning can solve difficult high dimensional tasks, by exploiting the expressive representation ...
In need for alternatives to conventional cooling devices, the magnetocaloric effect (MCE) is one of the most promising ...
The electromechanical coupling properties of ferroelectric materials are widely studied and used to design macro and ...
Future wireless communication systems are envisioned to meet stringent latency requirements to support control ...