Quasi-isentropic compression is an effective method to achieve high-density and high-temperature implosion in laser-driven inertial confinement fusion (ICF). However, it requires precise matching between the laser profile and the target structure. Designing the optimal laser profile and the corresponding target for ICF is a challenge due to the large number of parameters involved. In this paper, we present a novel method that combines random walk and Bayesian optimization. The basic sampling data for Bayesian optimization are a series of laser pulse profiles and target structures that can produce relatively high areal densities obtained by the random walk method. This approach reduces the number of samples required for Bayesian optimization and mitigates low efficiency in the latter stages of the random walk method. The method also reduces the randomness in the optimization process and enhances the optimization efficiency. It should have important applications in ICF research.
準等熵壓縮是實現激光驅動慣性約束聚變（ICF）中高密度、高溫內爆的有效方法。然而，它要求激光與靶結構之間的精確匹配。由於涉及大量參數，設計最優的激光波形及相應的靶結構是一項挑戰。本文提出了一種將隨機遊走與貝葉斯優化相結合的新方法。貝葉斯優化的基礎採樣數據來源於隨機遊走方法所生成的一系列能夠產生相對較高面密度的激光波形與靶結構。該方法減少了貝葉斯優化所需的採樣數量，並緩解了隨機遊走在後期階段效率低下的問題。同時，該方法也降低了優化過程中的隨機性，從而提高了優化效率。該方法有望在ICF研究中獲得重要應用。