Laser fusion has a potential to provide clean energy for humankind on the earth and in the space. Rayleigh-Taylor (RT) instability plays a critical role in the pursue of fusion ignition and high-gain burning. RT instability occurs when the density gradient and pressure gradient are in opposite directions. Its evolution can lead to adverse effects such as the mixing of the ablator layer with fusion fuel, and the mixing of cold fuel with the hot spot, thereby degrading the fusion performance. For the drive pulse(laser/x-ray) and target structures with more than 20 parameters, traditional simulations suffer from low optimization efficiency, and large discrepancies between simulations and experimental results. Consequently, they cannot meet the urgent demand for high-precision and high-efficiency optimization in laser fusion. In this work, we propose a machine learning method to suppress the RT instability by optimizing the drive pulse. Simulation results of MULTI-2D program indicate that it is possible to suppress the development of RT instability while keeping high implosion performance in both directly and indirectly drive fusion.

Machine Learning,Laser Fusion,Implosion Optimization,Drive Pulse