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Quantification of Uncertainty: Improving Efficiency and Technology = QUIET selected contributions /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Quantification of Uncertainty: Improving Efficiency and Technology/ edited by Marta D'Elia, Max Gunzburger, Gianluigi Rozza.
Reminder of title:	QUIET selected contributions /
other author:	D'Elia, Marta.
Description:	XI, 282 p. 113 illus., 90 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer mathematics. -
Online resource:	https://doi.org/10.1007/978-3-030-48721-8
ISBN:	9783030487218

Quantification of Uncertainty: Improving Efficiency and Technology = QUIET selected contributions /
Quantification of Uncertainty: Improving Efficiency and TechnologyQUIET selected contributions /[electronic resource] :edited by Marta D'Elia, Max Gunzburger, Gianluigi Rozza. - 1st ed. 2020. - XI, 282 p. 113 illus., 90 illus. in color.online resource. - Lecture Notes in Computational Science and Engineering,137 1439-7358 ;. - Lecture Notes in Computational Science and Engineering,103.

1. Adeli, E. et al., Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods -- 2. Brugiapaglia S., A compressive spectral collocation method for the diffusion equation under the restricted isometry property -- 3. D’Elia, M. et al., Surrogate-based Ensemble Grouping Strategies for Embedded Sampling-based Uncertainty Quantification -- 4. Afkham, B.M. et al., Conservative Model Order Reduction for Fluid Flow -- 5. Clark C.L. and Winter C.L., A Semi-Markov Model of Mass Transport through Highly Heterogeneous Conductivity Fields -- 6. Matthies, H.G., Analysis of Probabilistic and Parametric Reduced Order Models -- 7. Carraturo, M. et al., Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains -- 8. Boccadifuoco, A. et al., Uncertainty quantification applied to hemodynamic simulations of thoracic aorta aneurysms: sensitivity to inlet conditions -- 9. Anderlini, A.et al., Cavitation model parameter calibration for simulations of three-phase injector flows -- 10. Hijazi, S. et al., Non-Intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: a Comparison and Perspectives -- 11. Bulté, M. et al., A practical example for the non-linear Bayesian filtering of model parameters.

This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

ISBN: 9783030487218

Standard No.: 10.1007/978-3-030-48721-8doiSubjects--Topical Terms:

1199796
Computer mathematics.

LC Class. No.: QA71-90

Dewey Class. No.: 518

Quantification of Uncertainty: Improving Efficiency and Technology = QUIET selected contributions /
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