IMA 2013 UQ: Uncertainty Quantification in Materials Modeling

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Workshops

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Abstract

This IMA Hot Topics Workshop will bring together materials scientists specializing in modeling and simulation with researchers in uncertainty quantification (UQ) with the goal of identifying the main challenges in UQ for materials application. Some of the specific challenges in this area that will be addressed include:

  • Quantifying model form error that originates from lack of knowledge;
  • Uncertainties with multiple origins and at multiple scales that need to be quantified and propagated;
  • Combining models with disparate scales and descriptions (e.g., particle based vs. continuum), multiple fidelity, and stochastic outputs; and
  • Capturing intrinsic variability at atomic and microstructural levels.

There will be several types of presentations: materials modeling, UQ techniques, and UQ in materials. The workshop will also focus on the industry’s need to develop new materials using simulation-based methods. The format of the workshop will be a series of presentations targeted to a broad audience, as well as discussion sessions.

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Cite this work

Researchers should cite this work as follows:

  • (2014), "IMA 2013 UQ: Uncertainty Quantification in Materials Modeling," https://nanohub.org/resources/20302.

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Time

Location

Lind Hall, University of Minnesota, Minneapolis, MN

In This Workshop

  1. 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 models for smart material systems but the issues are relevant for a range of physical and biological...

  2. 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 lack of model refinement -- arises through assumptions made in physical models, numerical...

  3. 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 carbon dioxide is produced via reaction of water, the blowing agent, and isocyanate. In a competing...

  4. 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 example, is calculated from the phonon spectrum, and the melting temperature Tm can only be obtained...

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