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Stochastic models of uncertainties in computational mechanics [[electronic resource] /] / Christian Soize
| Stochastic models of uncertainties in computational mechanics [[electronic resource] /] / Christian Soize |
| Autore | Soize Christian |
| Pubbl/distr/stampa | Reston, Va., : American Society of Civil Engineers, : Engineering Mechanics Institute, 2012 |
| Descrizione fisica | 1 online resource (134 p.) |
| Disciplina | 003/.76 |
| Collana | Lecture notes in mechanics |
| Soggetto topico |
Stochastic models
Uncertainty (Information theory) Mechanics, Applied - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-7844-7686-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Cover""; ""Contents""; ""1 Introduction""; ""2 Short overview of probabilistic modeling of uncertainties and related topics""; ""2.1 Uncertainty and variability""; ""2.2 Types of approach for probabilistic modeling of uncertainties""; ""2.3 Types of representation for the probabilistic modeling of uncertainties""; ""2.4 Construction of prior probability models using the maximum entropy principle under the constraints defined by the available information""; ""2.5 Random Matrix Theory""; ""2.6 Propagation of uncertainties and methods to solve the stochastic dynamical equations""
""2.7 Identification of the prior and posterior probability models of uncertainties""""2.8 Robust updating of computational models and robust design with uncertain computational models""; ""3 Parametric probabilistic approach to uncertainties in computational structural dynamics""; ""3.1 Introduction of the mean computational model in computational structural dynamics""; ""3.2 Introduction of the reduced mean computational model""; ""3.3 Methodology for the parametric probabilistic approach of modelparameter uncertainties"" ""3.4 Construction of the prior probability model of model-parameter uncertainties""""3.5 Estimation of the parameters of the prior probability model of the uncertain model parameter""; ""3.6 Posterior probability model of uncertainties using output-predictionerror method and the Bayesian method""; ""4 Nonparametric probabilistic approach to uncertainties in computational structural dynamics""; ""4.1 Methodology to take into account both the model-parameter uncertainties and the model uncertainties (modeling errors)""; ""4.2 Construction of the prior probability model of the random matrices"" ""4.3 Estimation of the parameters of the prior probability model of uncertainties""""4.4 Comments about the applications and the validation of the nonparametric probabilistic approach of uncertainties""; ""5 Generalized probabilistic approach to uncertainties in computational structural dynamics""; ""5.1 Methodology of the generalized probabilistic approach""; ""5.2 Construction of the prior probability model of the random matrices""; ""5.3 Estimation of the parameters of the prior probability model of uncertainties"" ""5.4 Posterior probability model of uncertainties using the Bayesian method""""6 Nonparametric probabilistic approach to uncertainties in structural-acoustic models for the low- and medium-frequency ranges""; ""6.1 Reduced mean structural-acoustic model""; ""6.2 Stochastic reduced-order model of the computational structuralacoustic model using the nonparametric probabilistic approach of uncertainties""; ""6.3 Construction of the prior probability model of uncertainties""; ""6.4 Model parameters, stochastic solver and convergence analysis"" ""6.5 Estimation of the parameters of the prior probability model of uncertainties"" |
| Record Nr. | UNINA-9910462559703321 |
Soize Christian
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| Reston, Va., : American Society of Civil Engineers, : Engineering Mechanics Institute, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Uncertainty Quantification : An Accelerated Course with Advanced Applications in Computational Engineering / / by Christian Soize
| Uncertainty Quantification : An Accelerated Course with Advanced Applications in Computational Engineering / / by Christian Soize |
| Autore | Soize Christian |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XXII, 329 p. 110 illus., 86 illus. in color.) |
| Disciplina | 519.2 |
| Collana | Interdisciplinary Applied Mathematics |
| Soggetto topico |
Computer mathematics
Applied mathematics Engineering mathematics Probabilities Computational Science and Engineering Mathematical and Computational Engineering Probability Theory and Stochastic Processes |
| ISBN | 3-319-54339-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Fundamental Notions in Stochastic Modeling of Uncertainties and their Propagation in Computational Models -- Elements of Probability Theory -- Markov Process and Stochastic Differential Equation -- MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors -- Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties -- Brief Overview of Stochastic Solvers for the Propagation of Uncertainties -- Fundamental Tools for Statistical Inverse Problems -- Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics -- Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design -- Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media. |
| Record Nr. | UNINA-9910254309203321 |
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Soize Christian
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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