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Probabilistic Finite Element Model Updating Using Bayesian Statistics
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Probabilistic Finite Element Model Updating Using Bayesian Statistics : Applications to Aeronautical and Mechanical Engineering

by Ilyes Boulkaibet , Sondipon Adhikari , Tshilidzi Marwala

Book Details

Format Hardback or Cased Book
ISBN-10 1119153034
ISBN-13 9781119153030
Publisher John Wiley & Sons Inc
Imprint John Wiley & Sons Inc
Country of Manufacture MY
Country of Publication GB
Publication Date Nov 25th, 2016
Print length 248 Pages
Weight 520 grams
Dimensions 17.80 x 25.10 x 1.70 cms
Product Classification: Bayesian inference Mechanical engineering Aerospace & aviation technology
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Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering  Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK   Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering   Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering.   Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations.   The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

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