
[Illinois] 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

[Illinois] 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

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

Reproducing results from "PUQ: a code for nonintrusive 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 nonintrusive uncertainty propagation in computer simulations" to be published in...
http://nanohub.org/resources/22119

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 xy data
http://nanohub.org/resources/gptool

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...
http://nanohub.org/members/107467

Rohit Kaushal Tripathy
http://nanohub.org/members/106614

Andrew Dienstfrey
http://nanohub.org/members/105223

Bayesian Calibration
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
http://nanohub.org/resources/bayes

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 aminecured epoxies. We...
http://nanohub.org/resources/20312

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

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...
http://nanohub.org/members/102047

IMA 2013 UQ: DFTbased Thermal Properties: Three Levels of Error Management
02 Apr 2014  Online Presentations  Contributor(s): Kurt Lejaeghere
It is often computationally expensive to predict finitetemperature properties of a crystal from densityfunctional theory (DFT). The temperaturedependent thermal expansion coefficient α, for...
http://nanohub.org/resources/20311

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

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

Quantifying Uncertainties from the Grid in CFD Solutions
03 Jan 2012  Online Presentations  Contributor(s): Tom IP. Shih
This talk begins with a study on gridquality measures that assume gridinduced 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

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

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

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

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...
http://nanohub.org/resources/10694