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Probabilistic construction and numer...
~
Doostan, Alireza.
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Probabilistic construction and numerical analysis of model verification and validation.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Probabilistic construction and numerical analysis of model verification and validation./
Author:
Doostan, Alireza.
Description:
155 p.
Notes:
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6583.
Contained By:
Dissertation Abstracts International67-11B.
Subject:
Applied Mechanics. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3240697
ISBN:
9780542954054
Probabilistic construction and numerical analysis of model verification and validation.
Doostan, Alireza.
Probabilistic construction and numerical analysis of model verification and validation.
- 155 p.
Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6583.
Thesis (Ph.D.)--The Johns Hopkins University, 2007.
It the present manuscript, some recent developments in verification and validation (V&V) of predictive models are introduced. Verification is a mathematical concept which aims at assessing the accuracy of the solution of a given computational simulation compare to sufficiently accurate or analytical solutions. Validation, on the other hand, is a physics-based issue that aims at appraising the accuracy of a computational simulation compare to experimental data.
ISBN: 9780542954054Subjects--Topical Terms:
1018410
Applied Mechanics.
Probabilistic construction and numerical analysis of model verification and validation.
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Probabilistic construction and numerical analysis of model verification and validation.
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Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6583.
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Adviser: Roger Ghanem.
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Thesis (Ph.D.)--The Johns Hopkins University, 2007.
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It the present manuscript, some recent developments in verification and validation (V&V) of predictive models are introduced. Verification is a mathematical concept which aims at assessing the accuracy of the solution of a given computational simulation compare to sufficiently accurate or analytical solutions. Validation, on the other hand, is a physics-based issue that aims at appraising the accuracy of a computational simulation compare to experimental data.
520
$a
The proposed developments cast V&V in the form of an approximation-theoretic representation that permits their clear mathematical definition and resolution. In particular, three types of problems will be addressed. First, a priori and a posteriori error analysis of Wiener chaos spectral stochastic Galerkin scheme, a widely used tool for uncertainty propagation, are discussed. Second, a statistical procedure is developed in order to calibrate the uncertainty associated with parameters of a predictive model from experimental or model-based measurements. An important feature of such data-driven characterization algorithm, is in its ability to simultaneously represent both the intrinsic uncertainty and also the uncertainty due to data limitation. Third, a stochastic model reduction technique is proposed in order to increase the computational efficiency of spectral stochastic Galerkin schemes for the solution of complex stochastic systems.
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While the second part of this research is essential in model validation phase, the first part is particularly important as it provides one with basic components of the verification phase.
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School code: 0098.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3240697
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