The IEEE Nuclear and Plasma Sciences Society (NPSS) announced an LLNL team as the winner of its 2022 Transactions on Plasma Science Best Paper Award for their work applying machine learning to inertial confinement fusion (ICF) experiments. In the paper, lead author Kelli Humbird and co-authors propose a novel technique for calibrating ICF experiments by combining machine learning with experimental data via transfer learning (TL), a method in which models are trained on a task and then partially retrained to solve a separate but related task with limited data. The team, which includes Luc Peterson and Brian Spears of LLNL and Ryan McClarren from the University of Notre Dame, introduced the concept of hierarchical TL, where neural networks trained on low-fidelity models are calibrated to high-fidelity models and then applied to experimental data. The researchers applied the technique to a database of ICF simulations and experiments carried out at the University of Rochester’s Omega Laser Facility, finding the combination of deep neural networks with experiments resulted in better and more predictive models of ICF experiments than simulations alone. Read more at LLNL News.