Find information on common issues.
Ask questions and find answers from other users.
Suggest a new site feature or improvement.
Check on status of your tickets.
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...
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...
Rosenbrock Function for testing Uncertainty Quantification
0.0 out of 5 stars
13 Apr 2015 | Tools | Contributor(s): Martin Hunt
This tool is for testing and demonstrating ouq UQ code and Response Surfaces
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...
Gaussian process regression in 1D
26 Nov 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
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...
Rohit Kaushal Tripathy
18 Feb 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
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...
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...
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...
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...
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...
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...
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,...
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...
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.
ME 597A Lecture 13: Uncertainty Quantification of Molecular Dynamics Simulations
31 Jan 2011 | Online Presentations | Contributor(s): Alejandro Strachan
Guest lecturer: Alejandro Strachan.
ME597/AAE590: Introduction to Uncertainty Quantification
31 Jan 2011 | Courses | Contributor(s): Alina Alexeenko
The focus of the course is on the quantification of uncertainty in multiscale multiphsyics simulations for engineering analysis. The course introduces the student to the concepts of verification...