A Gentle Introduction to Uncertainty Quantification
19 Aug 2016 | | Contributor(s):: Ilias Bilionis
Efficient Exploration of Quantified Uncertainty in Granular Crystals
16 Mar 2016 | | Contributor(s):: Juan Camilo Lopez
This work presents a way of quantifying uncertainty in granular crystals in a computationally efficient way. To accomplish this, a low dimensional response surface is approximated through the method of active subspaces. Within this framework, special structure within the inputs is exploited to...
ME 597A Lecture 1: Introduction to V&V
07 Jun 2015 | | Contributor(s):: Jayathi Murthy, Alina Alexeenko
ME 597A Lecture 2: Sensitivity Analysis
07 Jun 2015 | | Contributor(s):: Sanjay Mathur
ME 597A Lecture 4: Generalized Polynomial Chaos for UQ I - The Galerkin Approach
07 Jun 2015 | | Contributor(s):: Dongbin Xiu, Akil Narayan
ME 597A Lecture 5: Generalized Polynomial Chaos for UQ II - The Collocation Approach
01 May 2015 | | Contributor(s):: Dongbin Xiu, Akil Narayan
Pertinent and Exciting Uncertainty Quantification Problem Spaces in Materials and Manufacturing
22 Jan 2016 | | Contributor(s):: Amanda Criner
A summary of a few exciting problem spaces will be given to demonstrate the breadth of pertinent uncertainty quantification problems to materials and manufacturing.
Predictive Simulations of Materials and Devices with Quantified Uncertainties
11 Feb 2016 | | Contributor(s):: Alejandro Strachan
Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation Using a L1 Minimization with Basis Rotation Algorithm
16 Dec 2015 | | Contributor(s):: Guang Lin
Biomolecules exhibit conformation fluctuations near equilibrium states, inducing uncertainty in various biological properties. We have developed a L1 minimization with basis rotation algorithm to quantify this uncertainty using a generalized polynomial chaos expansion on collective variables...
Quantifying Uncertainties in Physical Models
28 Aug 2017 | | Contributor(s):: Ilias Bilionis
Increasing modeling detail is not necessarily correlated with increasing predictive ability. Setting modeling and numerical discretization errors aside, the more detailed a model gets, the larger the number of parameters required to accurately specify its initial/boundary conditions, constitutive...
Reproducing results from "PUQ: a code for non-intrusive uncertainty propagation in computer simulations"
27 Mar 2015 | | Contributor(s):: Martin Hunt, Marisol Koslowski, Alejandro Strachan
In this document we use the nanoPLASTICITY in nanoHUB to reproduce results of the paper "PUQ: a code for non-intrusive uncertainty propagation in computer simulations" to be published in Computer Physics Communications. The paper abstract follows. We present a software package for the...
Rosenbrock Function for testing Uncertainty Quantification
13 Apr 2015 | | Contributor(s):: Martin Hunt
This tool is for testing and demonstrating ouq UQ code and Response Surfaces
Uncertainty Quantification in a Thermoelectric Device
26 Oct 2016 |
Posted by Tanya Faltens
Uncertainty Quantification in Materials Modeling: Topics on Uncertainty Quantification
04 Dec 2015 | | Contributor(s):: Alejandro Strachan
This is the seminar portion of the NCN and NEEDS 2015 Summer School consisting of presentations related to uncertainty quantification.
Uncertainty Quantification with Rappture
29 Sep 2016 | | Contributor(s):: Martin Hunt
Uncertainty Quantification (UQ) is a new feature in Rappture. This presentation discusses What is it? Why would you need it? How does it work? How do you use it?
View of Uncertainty from a Mathematician doing Research at a Chemical Company
16 Dec 2015 | | Contributor(s):: James Sturnfield