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Meeting MS&T24: Materials Science & Technology
Symposium Uncertainty Quantification Applications in Materials and Engineering
Presentation Title Introduction to Verification, Validation, and Uncertainty Quantification for Engineering Simulation
Author(s) Gavin Jones, Mark Andrews
On-Site Speaker (Planned) Gavin Jones
Abstract Scope Effective use of simulation requires confidence the simulation represents reality well. Challenges to achieving this confidence include the presence of model form and parameter uncertainty. Further, simulations are typically deterministic while the real world they are modeling is stochastic in nature. It is therefore important to understand how real-world uncertainties affect simulation results. Understanding and accounting for the degree to which a simulation represents reality is the domain of verification, validation, and uncertainty quantification (VVUQ) and helps simulation users make statements about the degree of credibility they have in their results, the probability of specific outcomes, and the risk associated with decisions and scenarios. This talk will provide an introduction to VVUQ and analyses that can support VVUQ efforts including sensitivity analysis, uncertainty propagation, and model calibration. Surrogate modeling, whereby a predictive model is trained of the simulation to make such analyses more efficient to perform will also be discussed.

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A Parametric Study of Optical Floating-Zone Crystal-Growth Furnace Through Modeling of Heat Transfer: Effect of Sample Properties and Environment Gas Pressure
Automating Engineering Design with UQ-Aware Scientific Learning
Bayesian Calibration of Cladding Creep Model Coefficients in the PAD5 Fuel Performance Code Using the Dakota Toolkit
Bayesian Protocols for High-Throughput Optimization of Kinematic Hardening Models Using Cyclic Microindentation Experiments
Introduction to Verification, Validation, and Uncertainty Quantification for Engineering Simulation
Quantification of Uncertainty in Microstructure Segmentation of Solid Oxide Cell Electrodes Using an Improved Watershed Methodology
Quantitative Analysis of Systematic Uncertainties in Empirical and Machine Learning Interatomic Potentials
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Uncertainty Quantification in Machine Learning Models with High-Dimensional Features and Large Sample Size
Uncertainty Quantification of Material Properties in Data-Poor Regimes Using Transfer Learning and Gaussian Process Regression
Unraveling Correlation between Interface Structure and Magnetic Properties of La1-xSrxCoO3−δ/La1-xSrxMnO3−δ Bilayers Using Neural Architecture Search and Deep Ensembles

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