Since completion of the National Ignition Facility (NIF) in 2010, more than 1030 experiments were carried out to achieve ignition. Though the experiments were unsuccessful in the first 8 years, the NIF has improved the experimental designs and achieved fusion yields from 55kJ, 170kJ to 1.35MJ since 2019, approaching to the ignition milestone. The designs are based on the experimental database, which has been widely used for optimization design, yield prediction, corrected simulation, etc. However, so far the published experimental data is very limited. Also, it is difficult to obtain a completion data matrix for analyzing and understanding the experimental designs of NIF experiments at each stage and to know how the NIF sets strategic priorities for each phase.

In this paper, we proposed an optimization method, which combines the PMM algorithm and trust region algorithm, to restore the missing NIF experimental data. Based on the completed data, the design principles of experiments on the NIF were analyzed, and the hot spot pressure was predicted by machine learning algorithms. The results may be helpful for the designs of laser fusion ignition experiments in China.