Jeffrey Florando | 17-ERD-029
Executive Summary
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 our nuclear deterrent without nuclear testing.
Publications, Presentations, Etc.
Bernstein, J. et al. 2019. "A Comparison of Material Flow Strength Models using Bayesian Cross-Validation." Computational Materials Science 169. doi: 10.1016/j.commatsci.2019.109098. LLNL-JRNL-758785.
Florando, J. N., et al. 2019. "Calibrating Strength Model Parameters Using Taylor Anvil and Stress-Strain Data." 2019 Machine Conference, Mumbai, India, May 2019. LLNL-ABS-761377.
Schmidt, K., et al. 2019. "Calibrating Strength Model Parameters Using Taylor Anvil Data." Joint Statistical Meeting, July/August 2019. LLNL-ABS-766548.
