Stochastic models of uncertainties in computational mechanics
Author(s)
Bibliographic Information
Stochastic models of uncertainties in computational mechanics
(Lecture notes in mechanics, 2)
American Society of Civil Engineers, c2012
- : pbk
Available at 1 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references (p. 91-107) and index (p. 109-125)
Description and Table of Contents
Description
Stochastic Models of Uncertainties in Computational Mechanics presents the main concepts, formulations, and recent advances in the use of a mathematical-mechanical modelling process to predict the responses of a real structural system in its environment. Computational models are subject to two types of uncertainties-variabilities in the real system and uncertainties in the model itself-so, to be effective, these models must support robust optimization, design, and updating.
A probabilistic approach to uncertainties is the most powerful, efficient, and effective tool for computational modeling. Chapters describe the methodology, construction, and estimation for using parametric, nonparametric, and generalised probabilistic approaches to the uncertainties in computational structural dynamics. Other chapters demonstrate the nonparametric probabilistic approach in the context of linear and nonlinear structural dynamics and in structural acoustics and vibration. A new methodology adapted to partial and limited experimental data is presented to identify random coefficients in high-dimension polynomial chaos expansions.
This compact guide to probabilistic approaches in computational modelling will be of interest to those working in computational sciences, especially structural engineers, who are developing and applying stochastic models of uncertainties to predict real-world structural systems.
by "Nielsen BookData"