In this talk I will introduce the topics of inertial confinement fusion and other high energy density experiments as an application space for uncertainty quantification and machine learning. In these experiments highenergy lasers are used to heat matter to incredible temperatures and pressures to study the processes that occur in astrophysical phenomenon and nuclear fusion.  Given the difficulty in modeling these experiments from first principles, there necessarily arises uncertainties due to model form error as well as experimental input uncertainties and material property uncertainty.  I will discuss how machine learning can be used to speed up the design of experiments to measure the spectral opacity of iron in a regime relevant to our understanding of our sun. Then I will talk about how we can combine uncertainty analysis and models of different fidelities to better understand the physics of inertial confinement fusion.

Speaker: Ryan Mcclarren

Ryan McClarren, Associate Professor of Aerospace and Mechanical Engineering at the University of Notre Dame, has applied simulation to understand, analyze, and optimize engineering systems throughout his academic career.