RAND Statistics Seminar Series
Uncertainty Quantification for Combining Experimental Data and Computer Simulations
Presented by Brian J. Williams, Los Alamos National Laboratory
Thursday, December 1, 2005
RAND Corporation, Santa Monica, CA
Please contact Teddi Sanchez if you would like to attend this seminar.
This work focuses on combining observations from field experiments with detailed computer simulations of a physical process to carry out inference. This typically involves calibration of parameters in the computer simulator as well as accounting for inadequate physics in the simulator. We consider applications in characterizing equation of state and material properties for which the field data and the simulator output are multivariate. For example, the data may be the velocity profile of a metal plate subjected to a plane shock wave at high strain rates. We use the basic framework of Kennedy and O'Hagan (2001). However, the size and multivariate nature of the data lead to computational challenges for implementing the framework. We consider adaptive basis methods (e.g. principal components, kernel smoothing) to achieve significant dimension reduction in the statistical formulation. We illustrate the proposed methodology with experimental data and simulations from a flyer plate experiment that was conducted to learn about material behavior in high strain rate environments.
This is joint work with Dave Higdon and Jim Gattiker of LANL.
Note: Washington, D.C. Conf. Rm. 8401; Pittsburgh Conf. Rm. 434
Attending a Seminar
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