Uncertainty Quantification And Model Calibration Ed. By Jan Peter Hessling
by Jan Peter Hessling /
2017 / English / PDF
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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.
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.
1 Introductory Chapter: Challenges of Uncertainty Quantification
2 Polynomial Chaos Expansion for Probabilistic Uncertainty Propagation
3 State‐of‐the‐Art Nonprobabilistic Finite Element Analyses
4 Epistemic Uncertainty Quantification of Seismic Damage Assessment
5 Uncertainty Quantification and Reduction of Molecular Dynamics Models
6 Bayesian Uncertainty Quantification for Functional Response
7 Fitting Models to Data: Residual Analysis, a Primer
8 An Improved Wavelet‐Based Multivariable Fault Detection Scheme
9 Practical Considerations on Indirect Calibration in Analytical Chemistry