Documents  62P30 | enregistrements trouvés : 7

- 210 p.
ISBN 978-0-7923-7309-4

Localisation : Colloque 1er étage (SHEF)

analyse de données statistiques # classification # discrimination # applications industrielles # analyse numérique # graphique d'ordinateur # arithmétique finie # algorithmes matriciels # reconnaissance de formes # biologie neuronale

65-06 ; 65D18 ; 65G40 ; 62-07 ; 68U05 ; 62P30 ; 62H30 ; 65G30 ; 68T10 ; 65F30 ; 92C20

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Research schools;Probability and Statistics

Uncertainty quantification (UQ) in the context of engineering applications aims aims at quantifying the effects of uncertainty in the input parameters of complex models on their output responses. Due to the increased availability of computational power and advanced modelling techniques, current simulation tools can provide unprecedented insight in the behaviour of complex systems. However, the associated computational costs have also increased significantly, often hindering the applicability of standard UQ techniques based on Monte-Carlo sampling. To overcome this limitation, metamodels (also referred to as surrogate models) have become a staple tool in the Engineering UQ community. This lecture will introduce a general framework for dealing with uncertainty in the presence of expensive computational models, in particular for reliability analysis (also known as rare event estimation). Reliability analysis focuses on the tail behaviour of a stochastic model response, so as to compute the probability of exceedance of a given performance measure, that would result in a critical failure of the system under study. Classical approximation-based techniques, as well as their modern metamodel-based counter-parts will be introduced. Uncertainty quantification (UQ) in the context of engineering applications aims aims at quantifying the effects of uncertainty in the input parameters of complex models on their output responses. Due to the increased availability of computational power and advanced modelling techniques, current simulation tools can provide unprecedented insight in the behaviour of complex systems. However, the associated computational costs have also increased ...

62P30 ; 65C05 ; 90B25 ; 62N05

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- xxvii; 514 p.
ISBN 978-0-89871-394-7

ASA-SIAM series on statistics and applied probability

Localisation : Enseignement RdC (CZIT)

commande de processus # méthodes statistiques # études de cas # semiconducteurs # industrie et commerce # procédés de fabrication # fiabilité

62P30 ; 90B25 ; 90B99 ; 62N99

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- xix; 404 p.
ISBN 978-0-387-24272-9

Springer series in operations research and financial engineering

Localisation : Ouvrage RdC (RESN)

application statistique # convergence faible # modélisation # processus de Poisson # distribution à queue lourde

60F17 ; 60F05 ; 60K25 ; 60K10 ; 60K30 ; 60G18 ; 60G51 ; 60G52 ; 60G55 ; 60G57 ; 60G70 ; 62G32 ; 62M10 ; 62E20 ; 62F10 ; 62F12 ; 62G30 ; 62G09 ; 62H20 ; 62P30 ; 62P05 ; 90B18 ; 90B15 ; 90B22 ; 68M20 ; 91B28 ; 91B30 ; 91B70 ; 62-02 ; 62Pxx ; 62-01

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- 798 p.

Collection de la direction des études et recherches d'électricité de france , 0067

Localisation : Ouvrage RdC (VILL)

industrie # fiabilité # informatique

90-02 ; 68-02 ; 62P30 ; 90B25

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- 591 p.
ISBN 978-1-58488-229-9

Localisation : Ouvrage RdC (MART)

statistique # MATLAB # handbook # statistique appliquée à l'industrie # informatique # échantillonage # variable aléatoire # analyse de données # méthode de Monté Carlo # inférence # partitionnage des données # estimation de densité # régression non paramétrique # chaîne de Markov # statistique spaciale

62P30 ; 62-01 ; 62-04

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