Tags: UQ

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  1. A Gentle Introduction to Uncertainty Quantification

    19 Aug 2016 | | Contributor(s):: Ilias Bilionis

  2. 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...

  3. ME 597A Lecture 1: Introduction to V&V

    07 Jun 2015 | | Contributor(s):: Jayathi Murthy, Alina Alexeenko

  4. ME 597A Lecture 2: Sensitivity Analysis

    07 Jun 2015 | | Contributor(s):: Sanjay Mathur

  5. ME 597A Lecture 4: Generalized Polynomial Chaos for UQ I - The Galerkin Approach

    07 Jun 2015 | | Contributor(s):: Dongbin Xiu, Akil Narayan

  6. ME 597A Lecture 5: Generalized Polynomial Chaos for UQ II - The Collocation Approach

    01 May 2015 | | Contributor(s):: Dongbin Xiu, Akil Narayan

  7. 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.

  8. Predictive Simulations of Materials and Devices with Quantified Uncertainties

    11 Feb 2016 | | Contributor(s):: Alejandro Strachan

  9. 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...

  10. 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...

  11. 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...

  12. 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

  13. Uncertainty Quantification in a Thermoelectric Device

    Collections | 26 Oct 2016 | Posted by Tanya Faltens

    http://nanohub.org/groups/materials/collections/saved-materials-science-runs

  14. 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.

  15. 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?

  16. View of Uncertainty from a Mathematician doing Research at a Chemical Company

    16 Dec 2015 | | Contributor(s):: James Sturnfield