Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering

✍ Scribed by Christian Soize (auth.)

Publisher: Springer International Publishing
Year: 2017
Tongue: English
Leaves: 344
Series: Interdisciplinary Applied Mathematics 47
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials.
Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available.
This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.

✦ Table of Contents

Front Matter....Pages i-xxii
Fundamental Notions in Stochastic Modeling of Uncertainties and Their Propagation in Computational Models....Pages 1-15
Elements of Probability Theory....Pages 17-40
Markov Process and Stochastic Differential Equation....Pages 41-59
MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors....Pages 61-76
Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties....Pages 77-132
Brief Overview of Stochastic Solvers for the Propagation of Uncertainties....Pages 133-139
Fundamental Tools for Statistical Inverse Problems....Pages 141-153
Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics....Pages 155-216
Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design....Pages 217-243
Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media....Pages 245-300
Back Matter....Pages 301-329

✦ Subjects

Computational Science and Engineering;Appl.Mathematics/Computational Methods of Engineering;Probability Theory and Stochastic Processes

📜 SIMILAR VOLUMES

Spectral Methods for Uncertainty Quantif

📁 Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics

✍ O. P. Le Maître, Omar M. Knio (auth.) 📂 Library 📅 2010 🏛 Springer Netherlands 🌐 English

Spectral Methods for Uncertainty Quantif

📁 Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics (Scientific Computation)

✍ Olivier Le Maitre, Omar M Knio 📂 Library 📅 2010 🏛 Springer 🌐 English

This book deals with the application of spectral methods to problems of uncertainty propagation and quanti?cation in model-based computations. It speci?cally focuses on computational and algorithmic features of these methods which are most useful in dealing with models based on partial differe

Uncertainty Quantification in Computatio

📁 Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines

✍ Francesco Montomoli 📂 Library 📅 2019 🏛 Springer International Publishing 🌐 English

This book introduces design techniques developed to increase the safety of aircraft engines, and demonstrates how the application of stochastic methods can overcome problems in the accurate prediction of engine lift caused by manufacturing error. This in turn addresses the issue of achieving r

Uncertainty Quantification in Computatio

📁 Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines

✍ Francesco Montomoli, Mauro Carnevale, Antonio D'Ammaro, Michela Massini, Simone 📂 Library 📅 2015 🏛 Springer International Publishing 🌐 English

This book introduces novel design techniques developed to increase the safety of aircraft engines. The authors demonstrate how the application of uncertainty methods can overcome problems in the accurate prediction of engine lift, caused by manufacturing error. This in turn ameliorates the dif

Probability in Electrical Engineering an

📁 Probability in Electrical Engineering and Computer Science: An Application-Driven Course

✍ Jean Walrand 📂 Library 📅 2021 🏛 Springer 🌐 English

This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques

Uncertainty Quantification in Computatio

📁 Uncertainty Quantification in Computational Fluid Dynamics

✍ Timothy Barth (auth.), Hester Bijl, Didier Lucor, Siddhartha Mishra, Christoph S 📂 Library 📅 2013 🏛 Springer International Publishing 🌐 English

Fluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review