The high-performance computing publication HPCwire announced LLNL as the winner of its Editor’s Choice award for Best Use of HPC in Energy for applying cognitive simulation (CogSim) methods to inertial confinement fusion (ICF) research. The award recognizes the team for progress in their machine learning-based approach to modeling ICF experiments performed at the National Ignition Facility (NIF) and elsewhere, which has led to the creation of faster and more accurate models of ICF implosions. The CogSim work addresses the need for better models that can fully utilize available datasets, can accurately estimate uncertainty, and can improve with additional data. Much of the CogSim work has been done on HPC machines including Sierra, Lassen, and Corona, using the open source projects Merlin, a custom deep-learning workflow tool, and the Livermore Big Artificial Neural Network toolkit (LBANN), a deep-learning training framework optimized for HPC.