Uncertainty Quantification An Accelerated Course with Advanced Applications in Computational Engineering /

This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties wi...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Soize, Christian (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
Σειρά:Interdisciplinary Applied Mathematics, 47
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.