Tags: uncertainty quantification

All Categories (1-20 of 32)

  1. 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,...

  2. Uncertainty Quantification in a Thermoelectric Device

    Collections | 26 Oct 2016 | Posted by Tanya Faltens


  3. 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?

  4. Uncertainty Quantification in Materials Modeling: Topics on Uncertainty Quantification

    21 Mar 2016 | | Contributor(s):: Alejandro Strachan

    This is the seminar portion of the NCN and NEEDS 2015 Summer School consisting of presentations related to uncertainty quantification.

  5. High Dimensional Uncertainty Quantification via Multilevel Monte Carlo

    04 Feb 2016 | | Contributor(s):: Hillary Fairbanks

    Multilevel Monte Carlo (MLMC) has been shown to be a cost effective way to compute moments of desired quantities of interest in stochastic partial differential equations when the uncertainty in the data is high-dimensional. In this talk, we investigate the improved performance of MLMC versus...

  6. Multilevel Markov Chain Monte Carlo for Uncertainty Quantification in Subsurface Flow

    04 Feb 2016 | | Contributor(s):: Christian Ketelsen

    The multilevel Monte Carlo method has been shown to be an effective variance reduction technique for quantifying uncertainty in subsurface flow simulations when the random conductivity field can be represented by a simple prior distribution. In state-of-the-art subsurface simulation the...

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

    22 Jan 2016 | | Contributor(s):: James Sturnfield

  8. 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.

  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. Piyush Pandita


  11. 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

  12. 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...

  13. Gaussian process regression in 1D

    26 Nov 2014 | | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve

    Use Gaussian processes to represent x-y data

  14. Ilias Bilionis

    Dr. Ilias Bilionis is an Assistant Professor at the School of Mechanical Engineering, Purdue University. His research is motivated by energy and material science applications and it focuses on the...


  15. Rohit Kaushal Tripathy


  16. Andrew Dienstfrey


  17. Bayesian Calibration

    18 Feb 2014 | | Contributor(s):: Martin Hunt, Benjamin P Haley, Jan Ebinger, Alejandro Strachan

    Given a model, input data for some paramaters and output data, calibrate unknown input parameters

  18. IMA 2013 UQ: Bayesian Calibration of Molecular Dynamics Simulations for Composite Materials Properties

    10 Feb 2014 | | Contributor(s):: Paul N. Patrone

    In this talk, I discuss ongoing research whose goal is to determine, via Bayesian inference, an ensemble of inputs that represents a class of commercially important amine-cured epoxies. We construct an analytical approximation (i.e. a surrogate or emulator) of the simulations, treating the input...

  19. IMA 2013 UQ: Foam Property Prediction from Process Modeling

    10 Feb 2014 |

    We are developing computational models to elucidate the injection, expansion, and dynamic filling process for polyurethane foam such as PMDI. The polyurethane is a chemically blown foam, where carbon dioxide is produced via reaction of water, the blowing agent, and isocyanate. In a competing...

  20. Guang Lin

    Guang Lin is an Assistant Professor in the Department of Mathematics & School of Mechanical Engineering at Purdue University.He is also an Affiliated Faculty in the Department of Applied...