Stochastic Methods in Engineering

Stochastic Methods in Engineering
Author: I. St Doltsinis
Publisher: WIT Press
Total Pages: 379
Release: 2012
Genre: Mathematics
ISBN: 1845646266

The increasing industrial demand for reliable quantification and management of uncertainty in product performance forces engineers to employ probabilistic models in analysis and design, a fact that has occasioned considerable research and development activities in the field. Notes on Stochastics eventually address the topic of computational stochastic mechanics. The single volume uniquely presents tutorials on essential probabilistics and statistics, recent finite element methods for stochastic analysis by Taylor series expansion as well as Monte Carlo simulation techniques. Design improvement and robust optimisation represent key issues as does reliability assessment. The subject is developed for solids and structures of elastic and elasto-plastic material, large displacements and material deformation processes; principles are transferable to various disciplines. A chapter is devoted to the statistical comparison of systems exhibiting random scatter. Where appropriate examples illustrate the theory, problems to solve appear instructive; applications are presented with relevance to engineering practice. The book, emanating from a university course, includes research and development in the field of computational stochastic analysis and optimization. It is intended for advanced students in engineering and for professionals who wish to extend their knowledge and skills in computational mechanics to the domain of stochastics. Contents: Introduction, Randomness, Structural analysis by Taylor series expansion, Design optimization, Robustness, Monte Carlo techniques for system response and design improvement, Reliability, Time variant phenomena, Material deformation processes, Analysis and comparison of data sets, Probability distribution of test functions.

The Analyses of Stationary Stochastic Processes Using Spectral and Autoregression Techniques

The Analyses of Stationary Stochastic Processes Using Spectral and Autoregression Techniques
Author: John N. Groff
Publisher:
Total Pages: 122
Release: 1972
Genre:
ISBN:

The report deals with the time and frequency domain analyses of stationary stochastic processes, i.e., a process whose statistics are time invariant over some interval of time. Algorithms and software have been developed which permit such analyses given either an ensemble of time histories which are representative of the process or merely a single time history of the process. The latter case is of particular interest since usually only limited data is available for such analyses. The report shows that if a process is gauss Markov in addition to being stationary then a single time history of sufficient length is adequate to perform frequency domain analysis or more precisely spectral analysis. The report also demonstrates the applicability of this methodology as an alternate approach to Monte Carlo simulation when suitable assumptions are satisfied. Such an application when feasible could result in a sizeable reduction in Monte Carlo budget requirements. (Author Modified Abstract).

Seminar on Stochastic Analysis, Random Fields and Applications

Seminar on Stochastic Analysis, Random Fields and Applications
Author: Robert C. Dalang
Publisher: Springer Science & Business Media
Total Pages: 314
Release: 1999-04-01
Genre: Mathematics
ISBN: 9783764361068

A collection of 20 refereed research or review papers presented at a six-day seminar in Switzerland. The contributions focus on stochastic analysis, its applications to the engineering sciences, and stochastic methods in financial models, which was the subject of a minisymposium.

An Introduction to Stochastic Modeling

An Introduction to Stochastic Modeling
Author: Howard M. Taylor
Publisher: Academic Press
Total Pages: 410
Release: 2014-05-10
Genre: Mathematics
ISBN: 1483269272

An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.