Uncertainty Quantification and Model Calibration

Jan Peter Hessling

ISBN 10: 9535132792 / ISBN 13: 9789535132790

Published by Intechopen, 2017

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.

Seller Inventory # 9789535132790

Bibliographic Details

• Title: Uncertainty Quantification and Model Calibration
• Publisher: Intechopen
• Publication Date: 2017
• Language: English
• ISBN 10: 9535132792
• ISBN 13: 9789535132790
• Binding: Buch
• Condition: Neu
• Dimensions: 266W x 185H x 20D millimeters 266 millimeters width by 185 millimeters height by 20 millimeters depth
• Weight: 706 grams

About this title

Synopsis

Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.