
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 61 - 90 of 99
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 ...
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 ...
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 ...
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 ...
Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...
Spacecraft missions of the European Space Agency (ESA) are required to meet specific probabilistic requirements ...
Future wireless communication systems are envisioned to meet stringent latency requirements to support control ...
Traditionally robots have been used in factories in predefined structured environments. Recently, robots are employed ...
In our project, we investigate the hardness of various instances of the shortest vector problem (SVP) which are the ...
In June 2017, ESA started systematic Level 2A processing of Sentinel-2 acquisitions over Europe using the cloud ...
The Bounded Support Spectral Solver (BoSSS) is developed as a flexible solver package to enable research in (mostly) ...
The Bounded Support Spectral Solver (BoSSS) is developed as a flexible solver package to enable research in (mostly) ...
The project investigated probabilistic motor skill learning for simulated robots. In detail the project looked into the ...