Name: Fundamentals of Uncertainty Quantification for Engineers: Methods and Models (en Inglés)
SKU: 64532594
Price: 3234.26 MXN
Availability: InStock
Author: Yan Wang Ph.D; Anh.V. Tran Ph.D.; David L. Mcdowell Ph.D.
ISBN: 9780443136610

portada Fundamentals of Uncertainty Quantification for Engineers: Methods and Models (en Inglés)

Fundamentals of Uncertainty Quantification for Engineers: Methods and Models (en Inglés)

Formato

Libro Físico

Editorial

Elsevier

Autor

Yan Wang Ph.D; Anh.V. Tran Ph.D.; David L. Mcdowell Ph.D.

Idioma

Inglés

N° páginas

434

Encuadernación

Tapa Blanda

ISBN13

9780443136610

Fundamentals of Uncertainty Quantification for Engineers: Methods and Models (en Inglés)

Yan Wang Ph.d; Anh.v. Tran Ph.d.; David L. Mcdowell Ph.d. (Autor) · Elsevier · Tapa Blanda

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Reseña del libro "Fundamentals of Uncertainty Quantification for Engineers: Methods and Models (en Inglés)"

Fundamentals of Uncertainty Quantification for Engineers: Methods and Models provides a comprehensive introduction to uncertainty quantification (UQ) accompanied by a wide variety of applied examples and implementation details to reinforce the concepts outlined in the book. Sections start with an introduction to the history of probability theory and an overview of recent developments of UQ methods in the domains of applied mathematics and data science. Major concepts of copula, Monte Carlo sampling, Markov chain Monte Carlo, polynomial regression, Gaussian process regression, polynomial chaos expansion, stochastic collocation, Bayesian inference, modelform uncertainty, multi-fidelity modeling, model validation, local and global sensitivity analyses, linear and nonlinear dimensionality reduction are included. Advanced UQ methods are also introduced, including stochastic processes, stochastic differential equations, random fields, fractional stochastic differential equations, hidden Markov model, linear Gaussian state space model, as well as non-probabilistic methods such as robust Bayesian analysis, Dempster-Shafer theory, imprecise probability, and interval probability. The book also includes example applications in multiscale modeling, reliability, fatigue, materials design, machine learning, and decision making.