Model-based simulation and optimization methods become increasingly important for the development of new products and more efficient processes. However, successful application of model-based methods demands for the development of predictive mathematical models and their quantitative validation. This complex task requires robust and efficient numerical methods for parameter estimation. Furthermore, advanced control of processes in real time needs fast and reliable numerical methods for state estimation.
Industrial practice shows that in order to realize the full potential of modelling complex processes we have to deal with a number of new challenges. Because of the high relevance of parameter and state estimation methods for current and future research and development, the Heidelberg Collaboratory for industrial Optimization (HCO) invites decision makers, researchers and practitioners from academia and industry to participate in this workshop.
This is the second workshop in a series on techniques for model-based optimization.
Sebastian F. Walter
Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp)
Interdisciplinary Center for Scientific Computing (IWR)
Komitee für mathematische Modellierung, Simulation und Optimierung (KoMSO)
MAThematics Center Heidelberg (MATCH)
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