Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis - Couverture souple

Doherty, John E; Hunt, Randall J; Tonkin, Matthew J

Couverture souple

ISBN 10 : 1500299987 ISBN 13 : 9781500299989

Editeur : CreateSpace Independent Publishing Platform, 2014

Synopsis

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncer- tainty analysis with regard to models can be very computa- tionally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system proper- ties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints).

Détails de l'édition

• Éditeur: CreateSpace Independent Publishing Platform
• Date d'édition: 2014
• Langue: anglais
• ISBN 10: 1500299987
• ISBN 13: 9781500299989
• Reliure: Broché
• Nombre de pages: 78
• Coordonnées du fabricant: Manufactured by Amazon on behalf of the author; https://www.amazon.fr/hz/contact-us; ; c/o Amazon Media EU S.à.r.l., 38 Avenue John F. Kennedy; Luxembourg; L-1855; Luxembourg