Unsupervised stochastic learning and reduced order modeling for global sensitivity analysis in cardiac electrophysiology models

Numerical simulations in electrocardiology are often affected by various uncertainties inherited from the lack of precise knowledge regarding input values including those related to the cardiac cell model, domain geometry, and boundary or initial conditions used in the mathematical modeling. Convent...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Computer methods and programs in biomedicine Jg. 255; S. 108311
Hauptverfasser:	El Moçayd, Nabil, Belhamadia, Youssef, Seaid, Mohammed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Ireland Elsevier B.V 01.10.2024
Schlagworte:	Algorithms Cardiac electrophysiology Cardiac simulation Computer Simulation Electrophysiological Phenomena Heart - physiology Humans Models, Cardiovascular Polynomial Chaos expansion Stochastic Processes Uncertainty Uncertainty quantification Unsupervised Machine Learning Unsupervised stochastic learning Uncertainty quantification Polynomial Chaos expansion Cardiac simulation Unsupervised stochastic learning Cardiac electrophysiology
ISSN:	0169-2607, 1872-7565, 1872-7565
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!