Tags: uncertainty quantification

All Categories (1-20 of 24)

  1. 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...

    http://nanohub.org/resources/23522

  2. 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...

    http://nanohub.org/resources/23518

  3. Piyush Pandita

    http://nanohub.org/members/122779

  4. 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

    http://nanohub.org/resources/uqtest

  5. 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...

    http://nanohub.org/resources/22119

  6. 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

    http://nanohub.org/resources/gptool

  7. 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...

    http://nanohub.org/members/107467

  8. Rohit Kaushal Tripathy

    http://nanohub.org/members/106614

  9. Andrew Dienstfrey

    http://nanohub.org/members/105223

  10. 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

    http://nanohub.org/resources/bayes

  11. 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...

    http://nanohub.org/resources/20312

  12. 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...

    http://nanohub.org/resources/20310

  13. 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...

    http://nanohub.org/members/102047

  14. IMA 2013 UQ: DFT-based Thermal Properties: Three Levels of Error Management

    02 Apr 2014 | Online Presentations | Contributor(s): Kurt Lejaeghere

    It is often computationally expensive to predict finite-temperature properties of a crystal from density-functional theory (DFT). The temperature-dependent thermal expansion coefficient α, for...

    http://nanohub.org/resources/20311

  15. IMA 2013 UQ: Probabilistic Hazard Mapping and Uncertainty Quantification Based on Granular Flow Simulations

    02 Apr 2014 | Online Presentations | Contributor(s): Elaine Spiller

    PDE models of granular flows are invaluable tools for developing probabilistic hazards maps for volcanic landslides, but they are far from perfect. Epistemic uncertainty -- uncertainty due to a...

    http://nanohub.org/resources/20305

  16. IMA 2013 UQ: Prediction Interval Construction for Smart Material Systems in the Presence of Model Discrepancy

    01 Apr 2014 | Online Presentations | Contributor(s): Ralph Smith

    In this presentation, we will discuss issues pertaining to the construction of prediction intervals in the presence of model biases or discrepancies. We will illustrate this in the context of...

    http://nanohub.org/resources/20304

  17. Quantifying Uncertainties from the Grid in CFD Solutions

    03 Jan 2012 | Online Presentations | Contributor(s): Tom I-P. Shih

    This talk begins with a study on grid-quality measures that assume grid-induced errors in a CFD solution at a cell is a function of the cell size and shape, the grid distribution around that cell,...

    http://nanohub.org/resources/12548

  18. Verification and Validation in Simulations of Complex Engineered Systems

    03 Jan 2012 | Online Presentations | Contributor(s): Robert Moser

    Computational simulation is a ubiquitous tool in engineering. Further, the explosion of computational capabilities over the last several decades has resulted in the use of computational models of...

    http://nanohub.org/resources/12525

  19. ME 597A Lecture 12: Uncertainty Propagation in a Multiscale Model of Nanocrystalline Plasticity

    01 Feb 2011 | Online Presentations | Contributor(s): Marisol Koslowski

    Guest lecturer: Marisol Koslowski.

    http://nanohub.org/resources/10742

  20. ME 597A Lecture 13: Uncertainty Quantification of Molecular Dynamics Simulations

    31 Jan 2011 | Online Presentations | Contributor(s): Alejandro Strachan

    Guest lecturer: Alejandro Strachan.  

    http://nanohub.org/resources/10693