TY  - CHAP
AU  - Jan Peter Hessling
ED  - Jan Peter Hessling
Y1  - 2017-07-05
PY  - 2017
T1  - Introductory Chapter: Challenges of Uncertainty Quantification
N2  - 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.
BT  - Uncertainty Quantification and Model Calibration
SP  - Ch. 1
UR  - https://doi.org/10.5772/intechopen.69517
DO  - 10.5772/intechopen.69517
SN  - 978-953-51-3280-6
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-16
ER  -