Mathematische Modellierung

Zeigt, wie industrielle Problemstellungen mathematisch modelliert und berechnet werden können

Gibt einen Einblick in die Arbeit des Fraunhofer-Instituts (ITWM)

Zeigt wie mathematische Modellierung eingesetzt werden kann

Federico Moretta is a chemical engineering graduate from Politecnico di Milano. Now he is a Ph.D. student at the school of Chemical Industry and Chemical Engineering at Politecnico di Milano. His main project is about the characterization and optimization of anaerobic digestion processes. He has published various research articles in literature and presented research projects in congresses about mathematical modelling and data science of fermentative reactions and microorganism meta

Philip Gerlee is a researcher in biomathematics and works on problems in cancer research, evolutionary dynamics and ecology. He earned his undergraduate degree in Physics and went on to pursue a Master’s degree in Complex and Adaptive systems at Chalmers University of Technology. His PhD was earned at University of Dundee  for theoretical research into the evolutionary dynamics of solid tumour growth. After completing his doctoral work he worked as a postdoc at the Niels Bohr Institute,

Eugene Zak is an applied mathematician and enterprise-level software developer. He holds a Ph.D. in Applied Mathematics from Moscow Institute of Physics and Technology, Russia, and received the INFORMS 2011 Edelman Prize in Operations Research and Analytics. His areas of expertise include mathematical modeling, optimization, and software design and development. Eugene has worked at Amazon, Alstom Grid, IBM, and a number of start-up companies.

Jeffrey T. Barton, PhD, is Professor of Mathematics in the Mathematics Department at Birmingham-Southern College. A member of the American Mathematical Society and Mathematical Association of America, his mathematical interests include approximation theory, analytic number theory, mathematical biology, mathematical modeling, and the history of mathematics.

Introduction, objectives.- Overview of uncertainty propagation methods.- Review of Probabilistic versus Fuzzy Approaches to Uncertainty Propagation in Geosciences.- Fuzzy set characterization of uncertainty (fuzzy variables).- Applications of uncertainty analyses on simplified models.- Applications of uncertainty analysis to 3D subsurface contamination problems.- Discussion and conclusions.