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Quantification of uncertainty = impr...

International Workshop "Quantification of Uncertainty: Improving Efficiency and Technology" ((2017 :)

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Quantification of uncertainty = improving efficiency and technology : QUIET selected contributions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantification of uncertainty/ edited by Marta D'Elia, Max Gunzburger, Gianluigi Rozza.
其他題名:	improving efficiency and technology : QUIET selected contributions /
其他題名:	QUIET 2017
其他作者:	D'Elia, Marta.
團體作者:	International Workshop "Quantification of Uncertainty: Improving Efficiency and Technology"
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xi, 282 p. :ill., digital ;24 cm.
內容註:	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. Bulte, M. et al., A practical example for the non-linear Bayesian filtering of model parameters.
Contained By:	Springer Nature eBook
標題:	Uncertainty (Information theory) - Congresses. - Mathematical models -
電子資源:	https://doi.org/10.1007/978-3-030-48721-8
ISBN:	9783030487218

Quantification of uncertainty = improving efficiency and technology : QUIET selected contributions /
Quantification of uncertaintyimproving efficiency and technology : QUIET selected contributions /[electronic resource] :QUIET 2017edited by Marta D'Elia, Max Gunzburger, Gianluigi Rozza. - Cham :Springer International Publishing :2020. - xi, 282 p. :ill., digital ;24 cm. - Lecture notes in computational science and engineering,1371439-7358 ;. - Lecture notes in computational science and engineering ;137..

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. Bulte, 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:

3461659
Uncertainty (Information theory)
--Mathematical models--Congresses.

LC Class. No.: Q375

Dewey Class. No.: 519.54

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