2014 SIAM Conference on Uncertainty Quantification

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2014 SIAM Conference on Unc...

2014 SIAM Conference on Uncertainty Quantification

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The 2014 SIAM Conference on Uncertainty Quantification was held March 31-April 3, 2014 at the Raleigh Marriot City Center, Raleigh, North Carolina, USA. The conference brings together mathematicians, statisticians, scientists, and engineers with an interest in development and implementation of uncertainty quantification methods. While applications of UQ in many fields are represented at the conference, the focal application for the 2014 meeting is Earth science. The meeting was held in cooperation with the American Statistical Association (ASA), GAMM Activity Group on Uncertainty Quantification (GAMM AG UQ), and American Geophysical Union (AGU).

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Quantifying Uncertainty in Multiscale Heterogenous Solid Earth Crustal Deformation Data to Improve Understanding of Earthquake Processes

Theory for Reduced Modelling

Uncertainty Propagation in CFD

Uncertainty Quantification in Bayesian Inversion

Uncertainty Quantification in Difficult Risk Models Built on Large Genetic Vectors

Uncertainty Without the Reverend Bayes

Advances in Markov Chain Monte Carlo Methods for Large-scale Inverse Problems - Part I of II

Advances in Markov Chain Monte Carlo Methods for Large-scale Inverse Problems - Part II of II

Bayesian Analysis of Multi-fidelity Computer Codes

Characterizing Sample Distribution Properties and their Impact on Experimental Design

Numerical Approximation of High-dimensional Stochastic Equations - Part I of IV

Numerical Approximation of High-dimensional Stochastic Equations - Part II of IV

Numerical Approximation of High-dimensional Stochastic Equations - Part III of IV

Numerical Approximation of High-dimensional Stochastic Equations - Part IV of IV

Numerical Methods for Uncertainty Quantification of Coupled Problems - Part I of V

Numerical Methods for Uncertainty Quantification of Coupled Problems - Part II of V

Numerical Methods for Uncertainty Quantification of Coupled Problems - Part III of V

Numerical Methods for Uncertainty Quantification of Coupled Problems - Part IV of V

Numerical Methods for Uncertainty Quantification of Coupled Problems - Part V of V

PDEs with Random Coefficients of lognormal Type and Applications to Subsurface Flow

The Reliability of Computational Research Findings: Reproductible Research, Uncertainty Quantification, and Verification & Validation

Uncertainty Quantification for Extreme-scale High Performance Computing

A Few Elements of Numerical Analysis for Elliptic PDEs with Random Coefficients of lognormal Type

A Posteriori Error Estimates for Statistical Computations with Differential Equations with Stochastic Parameters

Estimation of Prediction Uncertainties in Oil Reservoir Simulation using Bayesian and Proxy Modeling Techniques

KAUST SRI Center for Uncertainty Quantification in Computational Science & Engineering

Multi-resolution Models for Large Data Sets

Reduced Order Methods for Modelling and Computational Reduction in UQ Problems

Uncertainty Quantification Challenges in High-Performance Scientific Computing

Verification, Validation & Error Quantification, and Reproducible Computational Science: An Outline of a New Methodological Approach