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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Detalles Bibliográficos
Autores principales: Evangelou, Nikolaos, Wichrowski, Noah J, Kevrekidis, George A, Dietrich, Felix, Kooshkbaghi, Mahdi, McFann, Sarah, Kevrekidis, Ioannis G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Physical Sciences and Engineering
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802152/
https://www.ncbi.nlm.nih.gov/pubmed/36714862
http://dx.doi.org/10.1093/pnasnexus/pgac154

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802152/
https://www.ncbi.nlm.nih.gov/pubmed/36714862
http://dx.doi.org/10.1093/pnasnexus/pgac154

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