New Machine-Learning Tactic Sharpens NIF Shot Predictions

Inertial confinement fusion (ICF) experiments on NIF are extremely complex and costly, and it is challenging to accurately and consistently predict the outcome. But that is now changing, thanks to the work of LLNL design physicists. In a paper recently published in Physics of Plasmas, design physicist Kelli Humbird and her colleagues describe a new machine learning-based approach for modeling ICF experiments that results in more accurate predictions of NIF shots. The paper reports that machine learning models that combine simulation and experimental data are more accurate than the simulations alone, reducing prediction errors from as high as 110 percent to less than 7 percent. "This paper on cognitive simulation models leverages a technique called'transfer learning,' " said Humbird, lead author of the paper, "that lets us combine our simulation knowledge and previous experimental data into a model that is more predictive of future inertial confinement fusion experiments at NIF than simulations alone."