Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design

Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysisis not yet common practice and difficulties arising in the determination of uncertain parameters are typ-ically not assigned properly. This contribution provides a tutorial review on methods for identifiabilityanalysis, regularization techniques and optimal experimental design. A guideline for the analysis and clas-sification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniquesfor the regularization of experimental design problems resulting from ill-posed parameter estimationsare discussed. Applications are presented for three different case studies of increasing complexity.