Ruprecht-Karls-Universität Heidelberg
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[Optimization with PDEs: Parameter Estimation and Optimal Experimental Design] - [2015 Sommer]

Module Code
MH31
Name
Optimization with PDEs: Parameter Estimation and Optimal Experimental Design
Credit Points
8 CP
Workload
240
Duration
1 Semester
Cycle
0
Methods Lecture course 4 hours + exercise session 2 hours
Objectives Ability to numerically solve optimization problems with PDEs.
Content The lecture gives an introduction to the theory and numerics of optimization problems with PDEs. The following topics are covered: Estimation of parameters in elliptic and parabolic PDEs; Optimal experimental design with PDEs; Optimal control of PDEs.
Learning outcomes Learn the basic concepts to solve parameter estimation and optimal experimental design problems with models based on PDEs
Prerequisites
Suggested previous knowledge Basic concepts of numerical methods for ordinary and partial differential equations (ODEs and PDEs) are advantageous. Knowledge of optimization methods is not mandatory.
Assessments Solution of exercises and a final exam in written or oral form. Details will be given by the lecturer at the beginning of the course.
Literature Lecture notes: Optimierung mit partiellen Differentialgleichungen, T. Cararro, 2012
M. Hinze, R. Pinnau, M. Ulbrich, S. Ulbrich, Optimization with PDE Constraints, Springer, 2008
D. Ucinski, Optimal Measurement Methods for Distributed Parameter System Identification, Crc Pr Inc (2005)
H.W. Engl, M. Hanke, A. Neubauer, Regularization of Inverse Problems, Kluwer, 2008
F. Tröltzsch, Optimale Steuerung partieller Differentialgleichungen, Vieweg, 2009
D.G. Luenberger, Linear and Nonlinear Programming, Springer, Berlin, 2008
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