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About this Symposium
Meeting MS&T24: Materials Science & Technology
Symposium Uncertainty Quantification Applications in Materials and Engineering
Sponsorship ACerS Engineering Ceramics Division
TMS: Advanced Characterization, Testing, and Simulation Committee
Organizer(s) Mark J. Andrews, SmartUQ (retired)
Gavin Jones, SmartUQ
Scope Uncertainty Quantification is the science of assessing what is known and not known in a given analysis. It provides the analysts the realm of variation in the analytical response or solution given that input parameters may not be well characterized. Aside from understanding the plausible variation in the analytical responses, it also plays an important role in decision analytics. The scope for this symposium includes examples of applying Uncertainty Quantification methods to Material Science and Engineering analyses. It includes any of the following topics. Methods to quantify input parameter measurement uncertainty. Using Sensitivity Analysis to identify input parameters which have the greatest impact on the responses of interest. Model Calibration methods and results. Assessing model form uncertainty. Using Bayesian methods to quantify uncertainties. How quantifying uncertainties aides in decision making process.
Abstracts Due 05/15/2024
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

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
Tasmanian Toolkit for Uncertainty Quantification
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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