Kathleen Schmidt: Sequential Experimental Design for Materials Strength Model Calibration

Presenter: Katie Schmidt, UQ & Optimization Group Leader, Lawrence Livermore National Laboratory

Description: Due to the time and expense associated with physical experiments, there is significant interest in optimal selection of the conditions for future experiments. Selection based on reduction in parameter uncertainty provides a natural path forward. We consider this type of optimal sequential design in the context of Bayesian calibration of materials strength models with the strength model characterizing the evolving resistance of a material to permanent strain. This problem is particularly challenging because different types of experiments and associated diagnostics are employed across strain rate regimes. For lower-strain-rate experiments, stress-strain curves can be measured directly. For higher-strain-rate experiments, strength must be inferred (e.g., from the deformation of a cylinder of material in a Taylor cylinder experiment). We employ data fusion in our sequential design methodology to incorporate these multiple experimental modalities.

LLNL-ABS-835231 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Bio: Katie Schmidt is the UQ & Optimization Group Leader at Lawrence Livermore National Laboratory. She joined LLNL in 2016 after earning a PhD in Applied Mathematics from North Carolina State University. During her time at the lab, Katie has been involved in a variety of uncertainty quantification problems related to national security as well as outreach and education through LLNL’s Data Science Institute. Her research interests include mixed-effects models, Bayesian inference, sequential design, and sensitivity analysis.

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