
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,...

Uncertainty Quantification in a Thermoelectric Device
Collections 
26 Oct 2016 
Posted by Tanya Faltens
https://nanohub.org/groups/materials/collections/savedmaterialsscienceruns

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?

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.

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 highdimensional. In this talk, we investigate the improved performance of MLMC versus...

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 stateoftheart subsurface simulation the...

View of Uncertainty from a Mathematician doing Research at a Chemical Company
22 Jan 2016   Contributor(s):: James Sturnfield

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.

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...

Piyush Pandita
https://nanohub.org/members/122779

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

Reproducing results from "PUQ: a code for nonintrusive 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 nonintrusive uncertainty propagation in computer simulations" to be published in Computer Physics Communications. The paper abstract follows. We present a software package for the...

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 xy data

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

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

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

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

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 aminecured epoxies. We construct an analytical approximation (i.e. a surrogate or emulator) of the simulations, treating the input...

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...

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