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FunUQ for MD
22 Oct 2018 | Contributor(s):: Sam Reeve, Alejandro Strachan
Functional uncertainty quantification for molecular dynamics
Uncertainty Quantification Tutorial using Jupyter Notebooks
02 Oct 2018 | | 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,...
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, constitutive...
Uncertainty Quantification in a Thermoelectric Device
26 Oct 2016 |
Posted by Tanya Faltens
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 high-dimensional. 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 state-of-the-art 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...
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 non-intrusive 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 non-intrusive 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 x-y data
Rohit Kaushal Tripathy
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 amine-cured epoxies. We construct an analytical approximation (i.e. a surrogate or emulator) of the simulations, treating the input...