We are developing a statistical methodology to demonstrate how to use uncertainty quantification to optimize the experimental and performance design of advanced materials, develop more robust material models, and advance the materials qualification process. Understanding the fidelity and uncertainty associated with material models is integral to national goals in maintaining the nation's nuclear deterrent without nuclear testing.
Bernstein, J., et al. 2018. "Comparing the Predictive Performance of Material Strength Models in a Bayesian Framework." Conference on Data Analysis, Santa Fe, NM, March 2018. LLNL-ABS-745169.
Schmidt, K. L., et al. 2018. "Parameter Subset Selection for Mixed-Effects Models." International Journal for Uncertainty Quantification 6(5). doi: 10.1615/Int.J.UncertaintyQuantification.2016016469. LLNL-PRES-754903.
——— . 2018. "Sensitivity Analysis of Strength Models Using Bayesian Adaptive Splines." 20th Biennial APS Conference on Shock Compression of Condensed Matter, St. Louis, MO, July 2017. LLNL-PROC-737638.
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