Title: Bayesian Optimal Design of Pulsed Power Experiments

Abstract:

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. Motivated by a passion for transforming uncertainty into actionable insights, Kathryn leverages her extensive expertise in model validation, model form error quantification, and Bayesian analyses to drive innovative solutions that enhance research outcomes.

Kathryn earned her PhD in Computational Science, Engineering, and Mathematics, along with her M.S. in Computational and Applied Mathematics, both from The University of Texas at Austin. Her fascination with mathematical modeling began at the University of California, San Diego, where she completed her B.A. in Applied Mathematics.

When she is not immersed in data and algorithms, Kathryn enjoys the chaos of family life with her three children and three dogs. Looking ahead, Kathryn aspires to continue pushing the boundaries of computational science while encouraging others to confront ubiquitous uncertainty in their work.