Bayesian Optimal Design of Pulsed Power Experiments

Traditionally, there are two pillars of science: theory and experimentation. These two inform one another and lead scientists to make educated guesses and decisions toward advancing science. More recently, the driving force behind scientific advancement has not just focused on how much information can be learned, but how quickly. Additionally, experimental data can be costly and difficult to obtain. With these motivations in mind, the field of experimental design aims to maximize the information gained from as few experimental data points as possible. Computation has emerged as a third pillar of science to complement the traditional two and has been used to facilitate optimal experimental design. Sandia’s Z machine is the world’s most powerful and efficient laboratory radiation source. Z experiments often exhibit large current losses, so a principal uncertainty is how effectively current can be delivered. Power flow simulations are very intensive, making them infeasible to use in critical design and optimization studies. Developing a consistent picture of how losses develop and evolve would improve understanding of present-day experiments and better constrain circuit model representations, providing a basis for quantifying uncertainties in circuit models applied to Z and improve confidence in predictions of target performance. This presentation details the implementation of a Bayesian optimization study to maximize the information gained from Z experimental data and design. * 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. Maupin 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, she spends her time playing with her three girls and two dogs.