Inertial confinement fusion (ICF) experiments at LLNL's National Ignition Facility (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 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. Read more at NIF News.