Computer and Information Science » Numerical Analysis and Scientific Computing

Uncertainty Quantification and Model Calibration

Edited by Jan Peter Hessling, ISBN 978-953-51-3280-6, Print ISBN 978-953-51-3279-0, 224 pages, Publisher: InTech, Chapters published July 05, 2017 under CC BY 3.0 license
DOI: 10.5772/65579
Edited Volume

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.