Uncertainty Quantification and Predictive Computational Science A Foundation for Physical Scientists and Engineers /

• Κύριος συγγραφέας: McClarren, Ryan G. (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Θέματα: Physics.; Computer mathematics.; Statistics .; Applied mathematics.; Engineering mathematics.; Mathematical physics.; Computer simulation.; Numerical and Computational Physics, Simulation.; Computational Science and Engineering.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Mathematical and Computational Engineering.; Mathematical Applications in the Physical Sciences.; Simulation and Modeling.

This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-conse...

Πίνακας περιεχομένων:

• Part I Fundamentals
• Introduction
• Probability and Statistics Preliminaries
• Input Parameter Distributions
• Part II Local Sensitivity Analysis
• Derivative Approximations
• Regression Approximations
• Adjoint-based Local Sensitivity Analysis
• Part III Parametric Uncertainty Quantification
• From Sensitivity Analysis to UQ
• Sampling-Based UQ
• Reliability Methods
• Polynomial Chaos Methods
• Part IV Predictive Science
• Emulators and Surrogate Models
• Reduced Order Models
• Predictive Models
• Epistemic Uncertainties
• Appendices
• A. A cookbook of distributions.