9780443136610 - fundamentals of uncertainty quantification for engineers: methods and models von wang (4 Ergebnisse)
Fundamentals of Uncertainty Quantification for Engineers: Methods and Models
Wang Ph.D, Yan; Tran Ph.D., Anh.V.; Mcdowell Ph.D., David L.
- Softcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
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EUR 198,09
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Zustand: New.
Fundamentals of Uncertainty Quantification for Engineers: Methods and Models
Wang Ph.D, Yan; Tran Ph.D., Anh.V.; Mcdowell Ph.D., David L.
- Softcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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EUR 241,11
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Zustand: New. In.
- Softcover
Anbieter: moluna, Greven, Deutschlandmoluna
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EUR 246,58
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Zustand: New. Introduces all major topics of uncertainty quantification with engineering examples and implementation detailsFeatures examples from a wide variety of science and engineering disciplines (e.g., fluids, structural dynamics, materials, manufa.
Sprache: Englisch
Verlag: Elsevier - Health Sciences Division Jun 2025, 2025
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 346,10
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Taschenbuch. Zustand: Neu. Neuware - 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 wit…h 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.
