A5061771730- Smart Grid Energy Management
- Integrated Energy Systems Optimization
- Electric Power System Optimization
- Optimal Power Flow Distribution
- Energy Load and Power Forecasting
- Smart Grid Security and Resilience
- Power System Optimization and Stability
- 3D Shape Modeling and Analysis
- Microgrid Control and Optimization
- Medical Imaging Techniques and Applications
- Renewable Energy and Sustainability
- Distributed and Parallel Computing Systems
- Probabilistic and Robust Engineering Design
- Energy Efficiency and Management
- Radiomics and Machine Learning in Medical Imaging
- Electric Vehicles and Infrastructure
- Hybrid Renewable Energy Systems
- Advanced MRI Techniques and Applications
- Advanced Optical Network Technologies
- Advanced Control Systems Optimization
- Renewable energy and sustainable power systems
- Meteorological Phenomena and Simulations
- Infrastructure Resilience and Vulnerability Analysis
- Computational Physics and Python Applications
- Gaussian Processes and Bayesian Inference
Technical University of Darmstadt
2018-2025
Energy Sciences Network
2021
Siemens (Germany)
2009-2016
Max Planck Society
2005-2011
Technical University of Munich
2011
Max Planck Institute for Biological Cybernetics
2005-2009
For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the map obtained from a transmission scan, which uses rotating radionuclide source, or CT scan combined PET/CT scanner. In case PET/MRI scanners currently under development, insufficient space for source exists; can be calculated MR image instead. This task challenging because intensities correlate with proton densities and tissue-relaxation properties, rather than...
PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and method based on atlas registration pattern recognition (AT&PR). <b>Methods:</b> Eleven patients each underwent PET/CT study separate multibed MRI study. The uses combination of thresholds, Dixon fat–water segmentation, component analysis detect the lungs. images are...
Ensemble weather forecasts based on multiple runs of numerical prediction models typically show systematic errors and require postprocessing to obtain reliable forecasts. Accurately modeling multivariate dependencies is crucial in many practical applications, various approaches have been proposed where ensemble predictions are first postprocessed separately each margin then restored via copulas. These two-step methods share common key limitations, particular, the difficulty include...
Multi-modal image registration is a challenging problem in medical imaging. The goal to align anatomically identical structures; however, their appearance images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating that it matches second, reference by maximizing some similarity score between deformed and image. Instead of using universal, but priori fixed criterion mutual information, we propose...
The forecasting literature on intraday electricity markets is scarce and restricted to the analysis of volume-weighted average prices. These only admit a highly aggregated representation market. Instead, we propose forecast entire price distribution. We approximate this distribution in non-parametric way using dense grid quantiles. conduct study data from German market aim quantiles for last three hours before delivery. compare performance several linear regression models an ensemble neural...
Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., switches, RNAi, heterozygotes) expensive. It thus desirable to use identification method that effectively incorporates available prior knowledge - such as sparse connectivity and allows design maximal information gained from each one.Our main contributions are twofold: a for consistent inference network structure provided, incorporating about connectivity....
We study nonparametric regression between Riemannian manifolds based on regularized empirical risk minimization. Regularization functionals for mappings should respect the geometry of input and output manifold be independent chosen parametrization manifolds. define analyze three most simple regularization with these properties present a rather general scheme solving resulting optimization problem. As application examples we discuss interpolation sphere, fingerprint processing, correspondence...
In this work, a local multi-modal energy market is introduced to couple district heating and electric systems. the course of ongoing decarbonization systems, systems have integrate more volatile renewable energies, whereas in thermal demand for sustainable heat generation continuously increasing. Market-based coordination thermal-electric can help alleviate these challenges. an adequate representation conversion assets, e.g., pumps, achieved by introducing novel coupling orders market. These...
A long-lasting, large-scale power blackout has a huge impact on the infrastructure of public life, as well critical including electricity and water supply. At same time, it can be observed that share renewable energies, thus possibility self-sufficiency, increased enormously in recent years. This contribution focuses question to what extend citizens are willing their resources order make city more resilient. In reference Ostrom's concept club or common goods, shown if how private good...