A probabilistic graphical model based stochastic input model construction

Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimens...

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Vydáno v:	Journal of computational physics Ročník 272; s. 664 - 685
Hlavní autoři:	Wan, Jiang, Zabaras, Nicholas
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.09.2014
Témata:	ACCURACY ALGORITHMS CHAOS THEORY CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS Computation COMPUTERIZED SIMULATION Constraint-based algorithm Karhunen–Loève (KL) expansion Kernel PCA KERNELS LIMITING VALUES MATHEMATICAL MODELS MATHEMATICAL SOLUTIONS Model reduction NONLINEAR PROBLEMS Polynomial chaos expansion POLYNOMIALS Portable document format PROBABILISTIC ESTIMATION Probabilistic graphical model structure learning Probabilistic methods Probability theory Random variables RANDOMNESS STOCHASTIC PROCESSES Stochasticity Constraint-based algorithm Kernel PCA Probabilistic graphical model structure learning Polynomial chaos expansion Karhunen–Loève (KL) expansion
ISSN:	0021-9991, 1090-2716
On-line přístup:	Získat plný text
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