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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 analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for identifiability analysis, regularization techniques and optimal experimental design. A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniques for the regularization of experimental design problems resulting from ill-posed parameter estimations are discussed. Applications are presented for three different case studies of increasing complexity.

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Metadaten
Author:Diana C. López C., Tilman Barz, Stefan KörkelGND, Günter Wozny
DOI:https://doi.org/10.1016/j.compchemeng.2015.03.002
Parent Title (English):Computers and Chemical Engineering
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of first Publication:2015
Release Date:2022/11/26
Volume:77
First Page:24
Last Page:42
Institutes:Fakultät Informatik und Mathematik
Publication:Externe Publikationen
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG