Uncertainty quantification : an accelerated course with advanced applications in computational engineering / Christian Soize

• Autore: Soize, Christian
• Pubblicazione: Cham, : Springer, 2017
• Titolo uniforme: Uncertainty quantification
• Descrizione fisica: xxii, 329 p. : ill. ; 24 cm
• Soggetto topico: 15-XX - Linear and multilinear algebra; matrix theory [MSC 2020]; 35-XX - Partial differential equations [MSC 2020]; 37-XX - Dynamical systems and ergodic theory [MSC 2020]; 47-XX - Operator theory [MSC 2020]; 60-XX - Probability theory and stochastic processes [MSC 2020]; 62-XX - Statistics [MSC 2020]
• Soggetto non controllato: High Stochastic Dimension; MCMC Methods; Maximum Entropy Principle; Model Uncertainties; Model-parameter Uncertainties; Non-Gaussian Random Fields; Nonparametric Uncertainties; Polynomial Chaos Expansion; Random matrices; Robust Design; Statistical inverse problems
• Titolo autorizzato: Uncertainty quantification
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN00123409
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: http://doi.org/10.1007/978-3-319-54339-0
• Serie: Interdisciplinary applied mathematics New York [etc.] . -Springer , 1990- ; 47