Machine learning on the ignition threshold for inertial confinement fusion

Abstract

In inertial confinement fusion, the ignition threshold factor (ITF), defined as the ratio of the available shell kinetic energy to the minimum ignition energy, is an important metric for quantifying how far an implosion is from its performance cliff. Traditional ITF research is based on analytical theories with explicit scaling laws and parameters obtained by numerically fitting simulation data. This present study uses machine learning (ML) methods to train implicit but more reliable ITF expressions. One-dimensional numerical simulations are used to develop a dataset with 20 000 targets, in which alpha particle heating magnifies the fusion yield by a factor of 6.5. These targets are defined as marginal ignition targets whose ITF equals unity. ML models such as neural networks, support vector machines, and Gaussian processes are trained to connect the minimum ignition velocity vigt with other implosion parameters, yielding an ML-based ITF of (vimp/vigt)7.5, where vimp represents the implosion velocity. Then, these ML models are used to obtain curves of the ignition probability vs the ITF and improved ignition cliffs that show considerably better accuracy than traditional scaling laws, which are observed. The results demonstrate that ML methods have promising application prospects for quantifying ignition margins and can be useful in optimizing ignition target designs and practical implosion experiments.