HDS-LEE @ HIDA Trainee Network

Our doctoral students at HIDA Trainee Network

Hu Zhao

Project

Python-based Package for Probabilistic Simulation and Uncertainty Quantification for Earth Surface Processes

Home institute

AICES, RWTH Aachen University

Host institute  

High-performance computing department, German Aerospace Center (DLR)

Summary

The aim of this project is to leverage innovative software engineering technologies of the DLR-HPC group in order to improve the quality and efficiency of our Python-based probabilistic package for forward and inverse uncertainty quantification of earth surface processes, and further to integrate our application-oriented package with common data science ecosystem.

Anna Simson

Project

Towards real-time data integration on-board ice melting technologies - Combining historical ice coring data, sensor data on-board melting probes and data from process simulations into a unified description of the ambient ice

Home institute

AICES, RWTH Aachen University

Host institute

Prof. Dr. Olaf Eisen, Institution: AWI (Alfred Wegener Institute)

Summary

The goal of the project is to combine data from ice cores, from sensors on-board melting probes, and from process simulations into a unified description of the ambient ice. The research stay at the AWI is devoted to assemble and scout the ice coring data sets that have been acquired and processed by the Glaciology Section at the AWI. The combined database will be used to develop methods for the real-time data integration on-board ice melting technologies. 

Lisa Beumer

Project

Data Science in Nuclear Verification – Extracting Verification-relevant Information from Geospatial Big Data

Home institute

Institute of Energy and Climate Research - Nuclear Waste Management and Reactor Safety (IEK-6), Forschungszentrum Jülich

Host institute

German Aerospace Center (DLR), Earth Observation Center (EOC), Remote Sensing Technology Institute (IMF), Department EO Data Science

Summary

The objective of the project is to investigate the possibilities of data science driven satellite imagery processing in the context of nuclear verification. Data science methods for extracting relevant information and knowledge from big data archives of earth observation data will be identified and further developed.

The DLR-IMF's EO Data Science Department focuses on big data analytics, data intelligence and knowledge discovery, by developing advanced model-based processing algorithms and further exploring AI for EO.

The research stay aims at leveraging the capabilities and experiences of EO Data Science to further enhance data analytics for nuclear verification developed so far.

Leonardo Rydin Gorjao

Project

PowerDynSys – Power Dynamical Systems, a python package for dynamical system stability in power-grid systems

Home institute

IEK-STE, FZJ / Institute for Theoretical Physics, University of Cologne

Host institute  

German Aerospace Center - Institute of Networked Energy Systems (DLR-VE)

Summary

The goal of this project is to design an efficient, open-source Python library for numerical simulations of networked power systems which can easily be combined with existent power-grid models. The DLR-VE has been at the forefront of modelling and documenting networked power-grid systems in open source models. The target is to have a functional, easy-to-use ODE solver for commonly employed power-grid dynamics models, accessible to academic and industrial applications.

Projects within the context of HDS-LEE

Project:

GPU based chemistry simulations in volcanic ash clouds

Guest

Dr. Johannes Holke

Home institute

High-performance computing department, German Aerospace Center (DLR)

Host Institute

Forschungszentrum Jülich

Summary

In a current project with researchers at Forschungszentrum Jülich we develop a massively parallel simulation tool to model the advection and diffusion of volcanic ash plumes in the atmosphere using dynamic adaptive mesh refinement technologies. During the exchange stay at Jülich we plan to extend this model with GPU based chemistry simulation tools. This will enable us to leverage modern heterogeneous supercomputers to accurately simulate and predict the ongoing processes after a volcanic eruption.

More information about the HIDA Trainee Network

For more information about the HIDA Trainee Network, what exactly it is about, who can be funded and when deadlines are, please have a look here

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