On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output...

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Veröffentlicht in:	PNAS nexus 2022-09, Vol.1 (4), p.pgac154-pgac154
Hauptverfasser:	Evangelou, Nikolaos, Wichrowski, Noah J, Kevrekidis, George A, Dietrich, Felix, Kooshkbaghi, Mahdi, McFann, Sarah, Kevrekidis, Ioannis G
Format:	Artikel
Sprache:	eng
Schlagworte:	Physical Sciences and Engineering
Online-Zugang:	Volltext
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