Brandenburg on the Havel, Brandenburg, Germany
We are committed to energetic, environmental & social progress. We use mathematical modelling and optimization to foster technological transfer from science to industry. We have a broad scope of applications from technical to biochemical systems and processes, and aviation. We have long-standing expertise in - Complex systems identification, dynamic modeling, and optimization - Process analysis, design and optimization - Optimal experimental design for model discrimination/parameter estimation - Statistical data analysis and machine learning - Stochastic modeling and uncertainty propagation in nonlinear systems Current Project: - VITVI: Virtuelle Triebwerksentwicklung mit Verfahren der künstlichen Intelligenz Details: https://technik.th-brandenburg.de/forschung-und-kooperation/projekte/vit-vi/ Past Project: - GREEN: Ganzheitliche Lösungen zur regionalen Energiewende für Industrie und Kommune - WIR! REEgion now: Das Projekt - Konzeptphase 1.03.-30.11.2021 - befasst sich als regionales Wertschöpfungsbündnis mit den Innovationsfeldern Energie, Mobilität und Digitalisierung in Zusammenarbeit mit Prof.Claus Vielhauer Fachbereich Informatik und Medien THB / Verbund Technische Hochschule Brandenburg, Potsdam-Institut für Klimafolgenforschung e.V., Regionalentwicklungsgesesllschaft Nordwestbrandenburg mbh - EMIBEX (EFRE), MaxSynBio (MPG, BMBF)
As an engineer and physicist, I specialized in the modeling and analysis of complex physical systems with a current focus on energy technologies, fluid dynamics, and aerospace applications. A key area of my research is the study of aircraft engine icing—understanding how geometry, flow, and environmental conditions interact to influence ice accretion and its impact on performance and safety. To tackle these challenges, I develop and apply advanced machine learning methods and surrogate modeling approaches, enabling rapid evaluation of high-dimensional parameter spaces and robust design exploration. My work increasingly leverages in situ analytics and memory-efficient pipelines to couple simulation, data processing, and decision-making in real time. I am particularly interested in explainable and physically consistent AI, which allows domain experts to understand, trust, and act on model predictions. This is crucial in safety-critical contexts such as aviation or energy systems, where transparency and interpretability must accompany performance. My broader mission is to bridge fundamental physics, engineering insight, and intelligent algorithms—advancing sustainable technologies through interdisciplinary research and collaborative development.
statistical model identification, model based analysis, (bio)process & experimental design, nonlinear robustification of optimal control problems, nonlinear uncertainty propagation, reverse engineering, network reconstruction.
PhD position: model based analysis, process & experimental design, nonlinear robustification of optimal control problems, nonlinear uncertainty propagation, reverse engineering, network reconstruction.
teaching assistant for statistics and experimental design PhD project
R&D, see publication: Knowledge Based 2D Blade Design Using Multi-Objective Aerodynamic Optimization and a Neural Network A. Huppertz, P. M. Flassig, R. J. Flassig, M. Swoboda ASME Turbo Expo 2007: Power for Land, Sea, and Air (GT2007), Montreal, Canada; 05/2007