TIME: 3:30 PM

LOCATION: GMCS 314

SPEAKER: Kathryn Maupin, Sandia National Laboratory (Optimization and Uncertainty Quantification)

ABSTRACT: Computational simulation has allowed scientists to explore, observe, and test physical regimes previously thought to be unattainable. Bayesian analysis provides a natural framework for incorporating the uncertainties that undeniably exist in computational modeling. However, the ability to perform quality Bayesian and uncertainty analysis is often limited by the computational expense of first principles physics models. In the absence of a reliable low-fidelity physics model, phenomenological surrogate models can be used to mitigate this expense; however, phenomenological models may not adhere to known physics or properties. Furthermore, the interactions of complex physics in high-fidelity codes lead to dependencies between quantities of interest (QoIs) that are difficult to quantify and capture when individual surrogates are used for each observable. Predicting multiple QoIs with a single surrogate preserves valuable insights regarding the correlated behavior of the target observables and maximizes the information gained from available data. A method of constructing GPs that emulate multiple QoIs simultaneously is presented. As an exemplar, we consider Magnetized Liner Inertial Fusion, a fusion concept that relies on the direct compression of magnetized, laser-heated fuel by a metal liner to achieve thermonuclear ignition. The calibration of critical diagnostic metrics is complicated by sparse experimental data and expensive high-fidelity neutron transport models. The use of a surrogate is therefore warranted, the development of which raises long-standing issues in modeling and simulation, including calibration, validation, and uncertainty quantification. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

BIO: Kathryn Maupin is a Principal Member of the Technical Staff at Sandia National Laboratories. Her research focuses on model form error quantification and model validation. Broader research interests include Bayesian methods, sensitivity analysis, uncertainty quantification, and Bayesian optimal experimental design. Kathryn joined Sandia as a postdoc in 2016 and converted to a staff position in 2018. She received her Ph.D. in Computational Sciences, Engineering and Mathematics and her M.S. in Computational and Applied Mathematics from The University of Texas at Austin after completing her B.A. in Applied Mathematics at the University of California, San Diego. When she is not working, Kathryn spends her time playing with her three girls and two dogs.

HOST: Sustainable Horizons Institute