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Tags: uncertainty quantification

All Categories (1-20 of 31)

  1. 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

    http://nanohub.org/resources/gptool

  2. Ilias Bilionis

    Dr. Ilias Bilionis is an Assistant Professor at the School of Mechanical Engineeringhttp://nanohub.org/members/107467

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. Uncertainty Propagation in a Multiscale Model of Nanocrystalline Plasticity

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

    A full course, "Introduction to Uncertainty Quantification" is offered on memsHUB.org. ME597/AAE590: Introduction to Uncertainty Quantification NNSA Center for Prediction of Reliability,...

    http://nanohub.org/resources/10742

  12. Uncertainty Quantification of Molecular Dynamics Simulations

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

    A full course, "Introduction to Uncertainty Quantification" is offered on memsHUB.org. ME597/AAE590: Introduction to Uncertainty Quantification NNSA Center for Prediction of Reliability,...

    http://nanohub.org/resources/10693

  13. 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

  14. Uncertainty Quantification in Experiments

    16 Dec 2010 | Online Presentations | Contributor(s): Arvind Raman

    An overview is provided of how experimental data should be reported with true uncertainties. Examples from experiments on gas damping measurements in RF switches and for estimation of...

    http://nanohub.org/resources/10035

  15. Q-factor calculator with Uncertainty Quantification

    06 May 2009 | Tools | Contributor(s): Sruti Chigullapalli, Xiaohui Guo, Alina Alexeenko

    Calculates squeeze-film damping Q-factors of microcantilevers for arbitrary ambient pressures

    http://nanohub.org/resources/quq

  16. BNC Annual Research Review: An Introduction to PRISM and MEMS Simulation

    04 Jun 2008 | Online Presentations | Contributor(s): Jayathi Murthy

    This presentation is part of a collection of presentations describing the projects, people, and capabilities enhanced by research performed in the Birck Center, and a look at plans for the...

    http://nanohub.org/resources/4717

nanoHUB.org, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.