Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology
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Published By Springer International Publishing

9783030487201, 9783030487218

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Non-intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: A Comparison and Perspectives

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_10 ◽

2020 ◽

pp. 217-240

Author(s):

Saddam Hijazi ◽

Giovanni Stabile ◽

Andrea Mola ◽

Gianluigi Rozza

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Polynomial Chaos

Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_7 ◽

2020 ◽

pp. 153-170 ◽

Cited By ~ 1

Author(s):

Fabrizio Garotta ◽

Nicola Demo ◽

Marco Tezzele ◽

Massimo Carraturo ◽

Alessandro Reali ◽

...

Keyword(s):

Isogeometric Analysis ◽

Analysis Approach ◽

Reduced Order

Surrogate-Based Ensemble Grouping Strategies for Embedded Sampling-Based Uncertainty Quantification

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_3 ◽

2020 ◽

pp. 41-66

Author(s):

M. D’Elia ◽

E. Phipps ◽

A. Rushdi ◽

M. S. Ebeida

Keyword(s):

Uncertainty Quantification ◽

Grouping Strategies

Uncertainty Quantification Applied to Hemodynamic Simulations of Thoracic Aorta Aneurysms: Sensitivity to Inlet Conditions

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_8 ◽

2020 ◽

pp. 171-192

Author(s):

Alessandro Boccadifuoco ◽

Alessandro Mariotti ◽

Katia Capellini ◽

Simona Celi ◽

Maria Vittoria Salvetti

Keyword(s):

Uncertainty Quantification ◽

Thoracic Aorta ◽

Inlet Conditions ◽

Hemodynamic Simulations

A Compressive Spectral Collocation Method for the Diffusion Equation Under the Restricted Isometry Property

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_2 ◽

2020 ◽

pp. 15-40

Author(s):

Simone Brugiapaglia

Keyword(s):

Diffusion Equation ◽

Collocation Method ◽

Restricted Isometry Property ◽

Spectral Collocation Method ◽

Spectral Collocation

A Practical Example for the Non-linear Bayesian Filtering of Model Parameters

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_11 ◽

2020 ◽

pp. 241-272

Author(s):

Matthieu Bulté ◽

Jonas Latz ◽

Elisabeth Ullmann

Keyword(s):

Model Parameters ◽

Bayesian Filtering ◽

Non Linear

Conservative Model Order Reduction for Fluid Flow

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_4 ◽

2020 ◽

pp. 67-99

Author(s):

Babak Maboudi Afkham ◽

Nicolò Ripamonti ◽

Qian Wang ◽

Jan S. Hesthaven

Keyword(s):

Fluid Flow ◽

Model Order Reduction ◽

Order Reduction ◽

Model Order

Cavitation Model Parameter Calibration for Simulations of Three-Phase Injector Flows

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_9 ◽

2020 ◽

pp. 193-215

Author(s):

Alessandro Anderlini ◽

Maria Vittoria Salvetti ◽

Antonio Agresta ◽

Luca Matteucci

Keyword(s):

Parameter Calibration ◽

Cavitation Model ◽

Model Parameter ◽

Three Phase ◽

Injector Flows

Effect of Load Path on Parameter Identification for Plasticity Models Using Bayesian Methods

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_1 ◽

2020 ◽

pp. 1-13

Author(s):

Ehsan Adeli ◽

Bojana Rosić ◽

Hermann G. Matthies ◽

Sven Reinstädler

Keyword(s):

Parameter Identification ◽

Bayesian Methods ◽

Load Path

Piecewise Polynomial Approximation of Probability Density Functions with Application to Uncertainty Quantification for Stochastic PDEs

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology ◽

10.1007/978-3-030-48721-8_5 ◽

2020 ◽

pp. 101-127

Author(s):

Giacomo Capodaglio ◽

Max Gunzburger

Keyword(s):

Uncertainty Quantification ◽

Probability Density ◽

Polynomial Approximation ◽

Probability Density Functions ◽

Stochastic Pdes ◽

Density Functions ◽

Piecewise Polynomial ◽

Piecewise Polynomial Approximation

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and TechnologyLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Non-intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: A Comparison and Perspectives

Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains

Surrogate-Based Ensemble Grouping Strategies for Embedded Sampling-Based Uncertainty Quantification

Uncertainty Quantification Applied to Hemodynamic Simulations of Thoracic Aorta Aneurysms: Sensitivity to Inlet Conditions

A Compressive Spectral Collocation Method for the Diffusion Equation Under the Restricted Isometry Property

A Practical Example for the Non-linear Bayesian Filtering of Model Parameters

Conservative Model Order Reduction for Fluid Flow

Cavitation Model Parameter Calibration for Simulations of Three-Phase Injector Flows

Effect of Load Path on Parameter Identification for Plasticity Models Using Bayesian Methods

Piecewise Polynomial Approximation of Probability Density Functions with Application to Uncertainty Quantification for Stochastic PDEs

Lecture Notes in Computational Science and Engineering - Quantification of Uncertainty: Improving Efficiency and Technology
Latest Publications