ICF target optimization using generative AI

This paper explores the capabilities of generative artificial intelligence (AI) for target optimization in inertial confinement fusion for energy production. For demonstration purposes, the focus is on optimizing the laser illumination temporal profile assuming a spherical implosion and a given target structure. An optimization protocol is based on the generative AI tool and a dataset for a shock ignition scheme produced with a reference hydrodynamic code. In a first optimization process, the generative AI proposed a family of laser power profiles by introducing a plateau before the shock that doubles the energy gain value of the reference configuration. In a second optimization process, the number of parameters defining the laser power profile is increased according to the results of the first step. The generative AI then suggested more general solutions including multiple plateaus and classical profiles without shock that further double the gain for half the laser energy required. The suggested optimization method can be extended to other configurations of laser-target interaction.