Quantification of uncertainty : improving efficiency and technology : QUIET selected contributions / Marta D'Elia, Max Gunzburger, Gianluigi Rozza, editors.

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Bibliographic Details
Corporate Authors:	International Workshop "Quantification of Uncertainty: Improving Efficiency and Technology" Trieste, Italy, International School for Advanced Studies (Trieste, Italy)
Other Authors:	D'Elia, Marta, Gunzburger, Max D., Rozza, Gianluigi
Format:	eBook
Language:	English
Published:	Cham : Springer, 2020.
Series:	Lecture notes in computational science and engineering ; 137.
Subjects:	Uncertainty (Information theory) > Mathematical models > Congresses. Differential equations, Partial > Congresses. Maths for engineers. Computer modelling & simulation. Numerical analysis. Technology & Engineering > Engineering (General) Computers > Computer Simulation. Mathematics > Counting & Numeration. Differential equations, Partial Uncertainty (Information theory) > Mathematical models proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès.
Online Access:	Click for online access

Table of Contents:

Intro
Preface
Contents
About the Editors
Effect of Load Path on Parameter Identification for Plasticity Models Using Bayesian Methods
1 Introduction
2 Model Problem
3 Bayesian Identification
4 Numerical Results
4.1 Discussion of the Results
5 Summary
References
A Compressive Spectral Collocation Method for the Diffusion Equation Under the Restricted Isometry Property
1 Introduction
1.1 Main Contributions
1.2 Literature Review
1.3 Outline of the Paper
2 Elements of Compressive Sensing
2.1 Compressive Sensing and Greedy Recovery
2.2 Recovery Guarantees Based on the Restricted Isometry Property
3 Compressive Spectral Collocation
3.1 The Spectral Basis and the Collocation Grid
3.2 The Compressive Approach
4 Theoretical Analysis
4.1 Restricted Isometry Property
4.2 Recovery Guarantees (Discussion)
5 Numerical Experiments
5.1 Recovery of Sparse Solutions
5.2 Recovery of Compressible Solutions
6 Conclusions
References
Surrogate-Based Ensemble Grouping Strategies for Embedded Sampling-Based Uncertainty Quantification
1 Introduction
2 Preliminaries
2.1 PDEs with Random Input Parameters
2.2 Numerical Solution via Stochastic Collocation Methods
2.3 Numerical Solution via Ensembles
3 Grouping Strategies
3.1 Surrogate-Based Grouping
3.2 Parameter-Based Grouping
4 Numerical Tests
4.1 Anisotropic Diffusion
4.2 Anisotropic Advection-Diffusion
5 Conclusion
References
Conservative Model Order Reduction for Fluid Flow
1 Introduction
2 Model Order Reduction for Time Dependent Problems
3 Skew Symmetric and Centered Schemes for Fluid Flows
3.1 Conservation Laws
3.2 Incompressible Fluid
3.3 Compressible Fluid
3.4 Time Integration
4 Model Reduction of Fluid Flow
4.1 Assembling Nonlinear Terms and Time Integration
5 Numerical Experiments
5.1 Vortex Merging
5.2 2D Kelvin-Helmholtz Instability
5.3 1D Shock Problem
5.4 Continuous Variable Resonance Combustor
6 Conclusions
References
Piecewise Polynomial Approximation of Probability Density Functions with Application to Uncertainty Quantification for Stochastic PDEs
1 Introduction
2 Piecewise-Linear Polynomial Approximations of PDFs
2.1 The Piecewise-Linear Approximation of a PDF
2.2 Numerical Illustrations
3 Validation Through Comparisons with Known PDFs
3.1 A Smooth PDF with Known Support
3.2 A Smooth PDF with Unknown Support
3.3 A Non-smooth PDF
3.4 Bivariate Mixed PDF
4 Application to an Unknown PDFs Associated with a Stochastic PDE
5 Concluding Remarks
References
Analysis of Probabilistic and Parametric Reduced Order Models
1 Introduction
2 Parametric and Stochastic Models
3 Algebras of Random Variables
3.1 Specifying the Algebra
3.2 States and the Expectation Functional
3.3 More Examples

Quantification of uncertainty : improving efficiency and technology : QUIET selected contributions / Marta D'Elia, Max Gunzburger, Gianluigi Rozza, editors.

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