
Projekte
Hessische Wissenschaftlerinnen und Wissenschaftler unterschiedlichster Disziplinen benötigen Hochleistungsrechnen für ihre Forschung.
Hessische Wissenschaftlerinnen und Wissenschaftler unterschiedlichster Disziplinen benötigen Hochleistungsrechnen für ihre Forschung.
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Typical HPC applications represent simulations of large scientific problems. Usually, these applications alternate ...
Natural visuomotor control tasks such as pouring liquids into cups are trivial for humans but are challenging to model ...
The Software-Factory 4.0 (SF4.0) is a 4-year LOEWE1 project funded by the German State of Hesse and involves several ...
The message passing interface (MPI) is the de-facto standard for distributed high performance computing. However, it ...
Within the last decade, deep neural networks have attracted much attention from academia and industry. Such a wide ...
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications ...
Genetic Algorithms (GAs) are a popular heuristic optimization method inspired by biological evolution. We implemented a ...
Organ-like three-dimensional cell aggregates developed in the laboratory represent versatile cellular models for drug ...
Normative computational models of human sensorimotor behavior based on optimal feedback control with signal-dependent ...
Reinforcement Learning is a powerful approach to achieve optimal behaviour. However, it typically requires a manual ...
Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human’s actions ...
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy ...
DNA-PKcs is a gateway protein of the Non-Homologous End Joining DNA Repair Pathway. Which is the process of repairing ...
Ion channels play a fundamental key role in all living organisms and are crucial for the signal transduction of neurons ...
Empirical performance modeling is a proven instrument to analyze the scaling behavior of HPC applications. Using a set ...
This project is concerned with promoting a more efficient usage of the HPC Systems. The two major technologies used to ...
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the ...
Reinforcement Learning (RL) has proven to be an empirically very successful approach to solving sequential decision ...
The Nonparametric Off-Policy Policy Gradient (NOPG) is a policy gradient algorithm to solve reinforcement learning tasks ...
Field-Programmable Gate Arrays (FPGA) contain programmable logic elements that can accommodate application-specific ...
The aim of the project is to develop a new family of optimization algorithms to handle convex constraints and evaluate ...
FPGA cards are increasingly deployed to cloud data centers and made available to users as platform for the ...
Precise models of the system dynamics are crucial for model-based control and reinforcement learning (RL) in autonomous ...
Deep Reinforcement Learning can solve difficult high dimensional tasks, by exploiting the expressive representation ...
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...
Ion channels play a fundamental key role in all living organisms and are crucial for the signal transduction of neurons ...
This project aimed at solving multi-label classification problems using rule learning algorithms. Multi-label ...
The bursty nature of network traffic is one of the main reasons for congestion in data centers, since traffic loads are ...
In June 2017, the European Space Agency started systematic Level 2A processing of Sentinel- 2 acquisitions over Europe ...
Identifying scalability bugs in parallel applications is a vital but also laborious and expensive task. Empirical ...