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|a 1008986697
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|a 9783319645285
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|a 620
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|a UAMI
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|a Papadopoulos, Vissarion.
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1 |
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|a Stochastic Finite Element Methods :
|b an Introduction.
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| 260 |
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|a Cham :
|b Springer International Publishing,
|c 2017.
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| 300 |
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|a 1 online resource (151 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
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|a Mathematical Engineering
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|a Print version record.
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|a Preface -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Stochastic Processes -- 1.1 Moments of Random Processes -- 1.1.1 Autocorrelation and Autocovariance Function -- 1.1.2 Stationary Stochastic Processes -- 1.1.3 Ergodic Stochastic Processes -- 1.2 Fourier Integrals and Transforms -- 1.2.1 Power Spectral Density Function -- 1.2.2 The Fourier Transform of the Autocorrelation Function -- 1.3 Common Stochastic Processes -- 1.3.1 Gaussian Processes -- 1.3.2 Markov Processes -- 1.3.3 Brownian Process -- 1.3.4 Stationary White Noise
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| 505 |
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|a 1.3.5 Random Variable Case1.3.6 Narrow and Wideband Random Processes -- 1.3.7 Kanai -- Tajimi Power Spectrum -- 1.4 Solved Numerical Examples -- 1.5 Exercises -- 2 Representation of a Stochastic Process -- 2.1 Point Discretization Methods -- 2.1.1 Midpoint Method -- 2.1.2 Integration Point Method -- 2.1.3 Average Discretization Method -- 2.1.4 Interpolation Method -- 2.2 Series Expansion Methods -- 2.2.1 The Karhunen -- LoÃv̈e Expansion -- 2.2.2 Spectral Representation Method -- 2.2.3 Simulation Formula for Stationary Stochastic Fields
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|a 2.3 Non-Gaussian Stochastic Processes2.4 Solved Numerical Examples -- 2.5 Exercises -- 3 Stochastic Finite Element Method -- 3.1 Stochastic Principle of Virtual Work -- 3.2 Nonintrusive Monte Carlo Simulation -- 3.2.1 Neumann Series Expansion Method -- 3.2.2 The Weighted Integral Method -- 3.3 Perturbation-Taylor Series Expansion Method -- 3.4 Intrusive Spectral Stochastic Finite Element Method (SSFEM) -- 3.4.1 Homogeneous Chaos -- 3.4.2 Galerkin Minimization -- 3.5 Closed Forms and Analytical Solutions with Variability Response Functions (VRFs)
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|a 3.5.1 Exact VRF for Statically Determinate Beams3.5.2 VRF Approximation for General Stochastic FEM Systems -- 3.5.3 Fast Monte Carlo Simulation -- 3.5.4 Extension to Two-Dimensional FEM Problems -- 3.6 Solved Numerical Examples -- 3.7 Exercises -- 4 Reliability Analysis -- 4.1 Definition -- 4.1.1 Linear Limit-State Functions -- 4.1.2 Nonlinear Limit-State Functions -- 4.1.3 First- and Second-Order Approximation Methods -- 4.2 Monte Carlo Simulation (MCS) -- 4.2.1 The Law of Large Numbers -- 4.2.2 Random Number Generators -- 4.2.3 Crude Monte Carlo Simulation
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| 505 |
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|a 4.3 Variance Reduction Methods4.3.1 Importance Sampling -- 4.3.2 Latin Hypercube Sampling (LHS) -- 4.4 Monte Carlo Methods in Reliability Analysis -- 4.4.1 Crude Monte Carlo Simulation -- 4.4.2 Importance Sampling -- 4.4.3 The Subset Simulation (SS) -- 4.5 Artificial Neural Networks (ANN) -- 4.5.1 Structure of an Artificial Neuron -- 4.5.2 Architecture of Neural Networks -- 4.5.3 Training of Neural Networks -- 4.5.4 ANN in the Framework of Reliability Analysis -- 4.6 Numerical Examples -- 4.7 Exercises -- Appendix A Probability Theory
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| 500 |
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|a ""A.1 Axiomatic Probability Theory""
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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|a Finite element method.
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| 650 |
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0 |
|a Stochastic models.
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| 650 |
|
6 |
|a Méthode des éléments finis.
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| 650 |
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6 |
|a Modèles stochastiques.
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| 650 |
|
7 |
|a Finite element method
|2 fast
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| 650 |
|
7 |
|a Stochastic models
|2 fast
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| 700 |
1 |
|
|a Giovanis, Dimitris G.
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| 776 |
0 |
8 |
|i Print version:
|a Papadopoulos, Vissarion.
|t Stochastic Finite Element Methods : An Introduction.
|d Cham : Springer International Publishing, ©2017
|z 9783319645278
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| 830 |
|
0 |
|a Mathematical engineering.
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| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5116570
|z Texto completo
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|a Askews and Holts Library Services
|b ASKH
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|b EBLB
|n EBL5116570
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|b YANK
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