I have been at LLNL since 2011, first as a postdoctoral researcher and then as a staff scientist in the physics division. My research interests include high energy density physics, inertial confinement fusion, plasma equation of state and opacity calculations, Bayesian analysis, and uncertainty quantification. In recent years I have focused on the development and application of advanced data analysis, Bayesian model calibration, and uncertainty quantification for inertial confinement fusion experiments at the National Ignition Facility. I have also performed foundational research into deep learning, including uncertainty quantification, for scientific problems, and I am a focus area lead in a large university research alliance looking at materials properties under extreme conditions, with a focus on quantification and reduction of uncertainties in materials properties of radiation-hydrodynamics simulations.
- "The JAG inertial confinement fusion simulation dataset for multi-modal scientific deep learning", JA Gaffney et al. UC San Diego Library Digital Collections https://doi.org/10.6075/J0RV0M27
- "Making inertial confinement fusion models more predictive", JA Gaffney et al. Physics of Plasmas 26, 082704 (2019)
- "A review of equation-of-state models for intertial confinement fusion materials", JA Gaffney et al., High Energy Density Physics 28, 7-24 (2018)
- "Deep learning: A guide for practitioners in the physical sciences", BK Spears et al., Physics of Plasmas 25, 080901 (2018)