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Hessische Wissenschaftlerinnen und Wissenschaftler unterschiedlichster Disziplinen benötigen Hochleistungsrechnen für ihre Forschung.
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Displaying 61 - 90 of 132
Matrix Elements of Next-Generation Chiral Interactions
The goal of ab initio nuclear structure theory is the description of nuclei from first principles without uncontrolled ...
NaturwissenschaftenTeilchen, Kerne und Felder
Optimized Scaling of ISSM
In previous project we investigated on the scaling and the load balancing of the Ice-sheet and Sea-level System Model ...
IngenieurwissenschaftenInformatik
Performance Analysis of the Ice-Sheet and Sea-Level System Model (ISSM)
Earth modeling is a wide field of different scientific domains, e.g., ice-modeling. All domains have in common, that ...
IngenieurwissenschaftenInformatik
Performance Enginnering of the Confined Unconfined Aquifer System Model (CUAS)
In the context of earth system modeling the simulation of ice-sheets is an important aspect. Ice-sheet modeling contains ...
IngenieurwissenschaftenInformatik-
Performance Monitoring for Future Exascale Systems
Typical HPC applications represent simulations of large scientific problems. Usually, these applications alternate ...
IngenieurwissenschaftenInformatik 
Ab Initio No-Core Shell Model
The prime goal of ab initio nuclear structure theory is the description of correlated systems of many nucleons based on ...
NaturwissenschaftenTeilchen, Kerne und Felder
Automatic Code Parallelization
The Software-Factory 4.0 (SF4.0) is a 4-year LOEWE1 project funded by the German State of Hesse and involves several ...
IngenieurwissenschaftenInformatik-
Correctness Checking of MPI Derived Datatypes Using TypeART
The message passing interface (MPI) is the de-facto standard for distributed high performance computing. However, it ...
IngenieurwissenschaftenInformatik 
Designing an Efficient Neural Architecture Search
Within the last decade, deep neural networks have attracted much attention from academia and industry. Such a wide ...
IngenieurwissenschaftenInformatik
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative-Entropy Trust Regions
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications ...
IngenieurwissenschaftenInformatik
Effect of Infinite Time-Horizons on Genetic Algorithms
Genetic Algorithms (GAs) are a popular heuristic optimization method inspired by biological evolution. We implemented a ...
IngenieurwissenschaftenInformatik
Fully Open-Shell Medium-Mass Nuclei and Electromagnetic Observables for Ab Initio Nuclear Structure II
The ab initio description of nuclear structure phenomena has progressed tremendously over the past years. In particular ...
NaturwissenschaftenTeilchen, Kerne und Felder-
Inverse Reinforcement Learning by Matching Feature Distributions
Reinforcement Learning is a powerful approach to achieve optimal behaviour. However, it typically requires a manual ...
Engineering SciencesComputer Science 
Learning Physically Interactive Behaviours for Human-Robot Interaction
Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human’s actions ...
IngenieurwissenschaftenInformatik
Matrix Elements of New Families of Chiral Interactions
The goal of ab initio nuclear structure theory is the description of nuclei from first principles without uncontrolled ...
NaturwissenschaftenTeilchen, Kerne und Felder
Measure-Valued Derivatives for Reinforcement Learning
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy ...
IngenieurwissenschaftenInformatik
Modeling of Large Proteins in DNA Repair
DNA-PKcs is a gateway protein of the Non-Homologous End Joining DNA Repair Pathway. Which is the process of repairing ...
NaturwissenschaftenIngenieurwissenschaftenInformatik-
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 ...
IngenieurwissenschaftenInformatik 
Precision Scale Setting Directly at High Temperatures Using Gradient Flow
The overarching goal of our research project is the determination of the QCD topological susceptibility Xtop(T) at very ...
NaturwissenschaftenTeilchen, Kerne und Felder
Scalability of OpenMP
This project is concerned with promoting a more efficient usage of the HPC Systems. The two major technologies used to ...
IngenieurwissenschaftenInformatik
When to Trust Your Model in Model-based Value Expansion?
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the ...
IngenieurwissenschaftenInformatik
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
Reinforcement Learning (RL) has proven to be an empirically very successful approach to solving sequential decision ...
IngenieurwissenschaftenInformatik
Balloon Estimators for Improving and Scaling the Nonparametric Off-Policy Policy Gradient
The Nonparametric Off-Policy Policy Gradient (NOPG) is a policy gradient algorithm to solve reinforcement learning tasks ...
IngenieurwissenschaftenInformatik
Computationally-Intensive Subproblems in FPGA-Based Accelerator Design
Field-Programmable Gate Arrays (FPGA) contain programmable logic elements that can accommodate application-specific ...
IngenieurwissenschaftenInformatik-
Constraint Space Projection Reinforcement Learning
The aim of the project is to develop a new family of optimization algorithms to handle convex constraints and evaluate ...
IngenieurwissenschaftenInformatik 
Design-Space Exploration for FPGA-Accelerators in Cloud Environments
FPGA cards are increasingly deployed to cloud data centers and made available to users as platform for the ...
IngenieurwissenschaftenInformatik-
Evaluating and Improving Recurrent Kalman Networks
Precise models of the system dynamics are crucial for model-based control and reinforcement learning (RL) in autonomous ...
IngenieurwissenschaftenInformatik 
Interpretable Reinforcement Learning
Deep Reinforcement Learning can solve difficult high dimensional tasks, by exploiting the expressive representation ...
Engineering SciencesComputer Science
Large-Scale Configuration Interaction for Ab Initio Nuclear Structure
The goal of ab initio nuclear structure theory is the description of correlated systems of many nucleons based on the ...
NaturwissenschaftenTeilchen, Kerne und Felder-
Model Learning With Recurrent Kalman Networks
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...
IngenieurwissenschaftenInformatik
