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Fundamentals of Uncertainty Quantification for Engineers : Methods and Models
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Fundamentals of Uncertainty Quantification for Engineers: Methods and Models
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Fundamentals of Uncertainty Quantification for Engineers : Methods and Models
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Add to basketCondition: New. - Introduces all major topics of uncertainty quantification with engineering examples and implementation details- Features examples from a wide variety of science and engineering disciplines (e.g., fluids, structural dynamics, materials, manufacturing, .
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Fundamentals of Uncertainty Quantification for Engineers
Published by Elsevier - Health Sciences Division, US, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
Language: English
£ 268.81
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Add to basketPaperback. Condition: New. 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.
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Fundamentals of Uncertainty Quantification for Engineers: Methods and Models
Published by Elsevier Science Ltd, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
Language: English
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Fundamentals of Uncertainty Quantification for Engineers
Published by Elsevier - Health Sciences Division, US, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
Language: English
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Add to basketPaperback. Condition: New. 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.
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Uncertainty Quantification in Multiscale Materials Modeling
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Uncertainty Quantification in Multiscale Materials Modeling (Elsevier Series in Mechanics of Advanced Materials)
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Fundamentals of Uncertainty Quantification for Engineers
Published by Elsevier - Health Sciences Division, US, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
Language: English
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Add to basketPaperback. Condition: New. 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.
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Fundamentals of Uncertainty Quantification for Engineers
Published by Elsevier - Health Sciences Division, US, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
Language: English
£ 270.92
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Add to basketPaperback. Condition: New. 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.
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Uncertainty Quantification in Multiscale Materials Modeling
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Fundamentals of Uncertainty Quantification for Engineers: Methods and Models
Published by Elsevier - Health Sciences Division, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
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Fundamentals of Uncertainty Quantification for Engineers: Methods and Models
Published by Elsevier Science Ltd, 2025
ISBN 10: 0443136610 ISBN 13: 9780443136610
Language: English
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Uncertainty Quantification in Multiscale Materials Modeling
Published by Elsevier Science & Technology, Woodhead Publishing, 2020
ISBN 10: 0081029411 ISBN 13: 9780081029411
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales. Englisch.
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Uncertainty Quantification in Multiscale Materials Modeling
Published by Elsevier Science & Technology|Woodhead Publishing, 2020
ISBN 10: 0081029411 ISBN 13: 9780081029411
Language: English
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to pro.
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Uncertainty Quantification in Multiscale Materials Modeling
Published by Elsevier Science & Technology, Woodhead Publishing, 2020
ISBN 10: 0081029411 ISBN 13: 9780081029411
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
