TY - CHAP A1 - Shuxing Yang A2 - Fenfen Xiong A3 - Fenggang Wang ED1 - Jan Peter Hessling Y1 - 2017-07-05 PY - 2017 T1 - Polynomial Chaos Expansion for Probabilistic Uncertainty Propagation 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. 2 UR - https://doi.org/10.5772/intechopen.68484 DO - 10.5772/intechopen.68484 SN - 978-953-51-3280-6 PB - IntechOpen CY - Rijeka Y2 - 2020-10-20 ER -