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On estimating the error in stochasti...

Pellissetti, Manuel Francesco.

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On estimating the error in stochastic model-based predictions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	On estimating the error in stochastic model-based predictions./
Author:	Pellissetti, Manuel Francesco.
Description:	219 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0859.
Contained By:	Dissertation Abstracts International64-02B.
Subject:	Engineering, Civil. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3080743

On estimating the error in stochastic model-based predictions.
Pellissetti, Manuel Francesco.

On estimating the error in stochastic model-based predictions. - 219 p.

Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0859.

Thesis (Ph.D.)--The Johns Hopkins University, 2003.

In the rational prediction of the behavior of physical systems, models are often relied upon. These predictive tools are calibrated in terms of parameters, on the basis of data. A recurrent phenomenon in this context is the random scatter in model parameters. Stochastic models have thus been developed, in which the parameters are treated as a random entity. The probabilistic characterization of the parameters is often hampered by practical limitations and induces inaccuracies in the stochastic predictions of the response.Subjects--Topical Terms:

783781
Engineering, Civil.

On estimating the error in stochastic model-based predictions.
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035 $a (UnM)AAI3080743
035 $a AAI3080743
040 $a UnM $c UnM
100 1 $a Pellissetti, Manuel Francesco. $3 1949449
245 1 0 $a On estimating the error in stochastic model-based predictions.
300 $a 219 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0859.
500 $a Adviser: Roger Ghanem.
502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2003.
520 $a In the rational prediction of the behavior of physical systems, models are often relied upon. These predictive tools are calibrated in terms of parameters, on the basis of data. A recurrent phenomenon in this context is the random scatter in model parameters. Stochastic models have thus been developed, in which the parameters are treated as a random entity. The probabilistic characterization of the parameters is often hampered by practical limitations and induces inaccuracies in the stochastic predictions of the response.
520 $a This thesis reports a novel methodology to estimate the error in stochastic model-based predictions. It relies on the response representation in a Polynomial-Chaos basis. The error is approximated via Taylor expansion and thus hinges on the explicit computation of the stochastic response gradient. The computed error estimate sheds light on the sensitivity of particular response statistics with respect to statistics of the stochastic parameters. This helps to raise the confidence in the model predictions.
520 $a The method is demonstrated on two model problems, involving a Bernoulli beam with random bending rigidity and the potential flow in a porous medium with random conductivity. In both cases the parameters are modelled as a random field and are discretized with the Karhunen-Loeve expansion. The finite element method is used for the spatial discretization.
590 $a School code: 0098.
650 4 $a Engineering, Civil. $3 783781
650 4 $a Applied Mechanics. $3 1018410
650 4 $a Operations Research. $3 626629
690 $a 0543
690 $a 0346
690 $a 0796
710 2 0 $a The Johns Hopkins University. $3 1017431
773 0 $t Dissertation Abstracts International $g 64-02B.
790 1 0 $a Ghanem, Roger, $e advisor
790 $a 0098
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3080743