Tags: uncertainty quantification

All Categories (1-20 of 32)

  1. Quantifying Uncertainties in Physical Models

    28 Aug 2017 | Online Presentations | 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...

    https://nanohub.org/resources/27200

  2. Uncertainty Quantification in a Thermoelectric Device

    Collections | 26 Oct 2016 | Posted by Tanya Faltens

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

  3. Uncertainty Quantification with Rappture

    29 Sep 2016 | Presentation Materials | 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?

    https://nanohub.org/resources/25053

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

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

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

    https://nanohub.org/resources/23176

  5. High Dimensional Uncertainty Quantification via Multilevel Monte Carlo

    04 Feb 2016 | Online Presentations | 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...

    https://nanohub.org/resources/23522

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

    04 Feb 2016 | Online Presentations | 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...

    https://nanohub.org/resources/23518

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

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

    https://nanohub.org/resources/23289

  8. Pertinent and Exciting Uncertainty Quantification Problem Spaces in Materials and Manufacturing

    22 Jan 2016 | Online Presentations | 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.

    https://nanohub.org/resources/23416

  9. Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation Using a L1 Minimization with Basis Rotation Algorithm

    16 Dec 2015 | Online Presentations | 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...

    https://nanohub.org/resources/23284

  10. Piyush Pandita

    https://nanohub.org/members/122779

  11. Rosenbrock Function for testing Uncertainty Quantification

    13 Apr 2015 | Tools | Contributor(s): Martin Hunt

    This tool is for testing and demonstrating ouq UQ code and Response Surfaces

    https://nanohub.org/resources/uqtest

  12. Reproducing results from "PUQ: a code for non-intrusive uncertainty propagation in computer simulations"

    27 Mar 2015 | Papers | 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...

    https://nanohub.org/resources/22119

  13. Gaussian process regression in 1D

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

    Use Gaussian processes to represent x-y data

    https://nanohub.org/resources/gptool

  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...

    https://nanohub.org/members/107467

  15. Rohit Kaushal Tripathy

    https://nanohub.org/members/106614

  16. Andrew Dienstfrey

    https://nanohub.org/members/105223

  17. Bayesian Calibration

    05 Jun 2014 | Tools | 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

    https://nanohub.org/resources/bayes

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

    28 May 2014 | Online Presentations | 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...

    https://nanohub.org/resources/20312

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

    28 May 2014 | Online Presentations

    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...

    https://nanohub.org/resources/20310

  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...

    https://nanohub.org/members/102047