Model Based Parameter Estimation Theory and Applications [Paperback]

This judicious selection of articles combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts. These include fields as diverse as biology, medicine, chemistry, environmental physics, image processing and computer vision. The material chosen was presented at a multidisciplinary workshop on parameter estimation held in 2009 in Heidelberg. The contributions show how indispensable efficient methods of applied mathematics and computer-based modeling can be to enhancing the quality of interdisciplinary research.

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The use of scientific computing to model, simulate, and optimize complex processes has become a standard methodology in many scientific fields, as well as in industry. Demonstrating that the use of state-of-the-art optimization techniques in a number of research areas has much potential for improvement, this book provides advanced numerical methods and the very latest results for the applications under consideration.

This book features papers from a workshop on parameter estimation held in 2009 in Heidelberg. It combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts.Parameter Estimation and Optimum Experimental Design for Differential Equation Models: H.G. Bock, St. K?rkel, J.P. Schl?der.- Adaptive Finite Element Methods for Parameter Identification Problems: B. Vexler.- Gauss-Newton Methods for Robust Parameter Estimation: T. Binder, E. Kostina.- An Optimal Scanning Sensor Activation Policy for Parameter Estimation of Distributed Systems: D. Uc?nski.- Interaction between Experiment, Modeling and Simulation of Spatial Aspects in the JAK2/STAT5 Signaling Pathway: E. Friedmann, A. C. Pfeifer, R. Neumann, U. Klingm?ller , R. Rannacher.- The Importance and Challenges of Bayesian Parameter Learning in Systems Biology: J. Mazur, L. Kaderali.- Experiment Setups anlc7