
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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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 ...
Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...
With increasing system performance and complexity, it is becoming increasingly crucial to examine the scaling behavior ...
The project focuses on the cryptanalysis of the Computational Supersingular Isogeny (CSSI) problem which underlies the ...
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
The exaFOAM project (https://exafoam.eu) aims at overcoming the current limitations of Computational Fluid Dynamics (CFD ...
In variational inference, we want to approximate an intractable target distribution (often given as a posterior ...
Recent developments in the field of complex systems have shown that real-world multiagent systems are often not ...
In previous project we investigated on the scaling and the load balancing of the Ice-sheet and Sea-level System Model ...
Earth modeling is a wide field of different scientific domains, e.g., ice-modeling. All domains have in common, that ...
In the context of earth system modeling the simulation of ice-sheets is an important aspect. Ice-sheet modeling contains ...
Typical HPC applications represent simulations of large scientific problems. Usually, these applications alternate ...
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 ...