Uncertainty quantification and predictive computational science : a foundation for physical scientists and engineers /: a foundation for physical scientists and engineers. (2018)

• Record Type: Book
• Further Information: Note: Ryan G. McClarren.
• Authors: McClarren, Ryan G
• Contents: 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.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2018
• Extent: 1 online resource (xvii, 345 pages), illustrations (some color)
• Subjects: 003/.54; Uncertainty (Information theory); 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; Uncertainty (Information theory); Electronic books
• Languages: English
• ISBNs: 9783319995250; 3319995251
• Related ISBNs: 9783319995243
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (SpringerLink, viewed December 5, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.