Mitigating RT instability and enhancing stagnation performance via optimization of laser pulse guided by machine learning

Abstract Rayleigh–Taylor (RT) instability is a major challenge in laser fusion. In this work, we propose an implosion optimization method that simultaneously suppresses RT instability and enhances implosion performance. The genetic algorithm is employed together with the MULTI-2D hydrodynamic code to optimize the laser pulse. Results show that for a 20-mode perturbation with an initial amplitude of 0.5 μ m, the optimized pulse can reduce the ablative RT instability amplitude by nearly 60%. In addition, the optimized waveform increases the stagnated areal density by 16% for central ignition scheme and 30% for the double-cone ignition scheme, creating favorable conditions for ignition. The optimized pulse shape also effectively suppresses multimode instabilities caused by random seeds and increase the neutron yield by 20% for the heated C 8 D 8 fuel. Analysis of the optimization outcomes reveals that the genetic algorithm effectively finds laser waveforms with higher adiabat and faster implosion velocities. This kind of implosion help to reduce the RT instability and enhance the areal density at stagnation.