A multi-fidelity active-learning optimizer found a 25 kJ laser-direct-drive target whose hydrodynamically scaled 2 MJ simulation burns with about 217x alpha amplification, far more than the 1D-optimized design's 17x.
Development of the indirect-drive approach to inertial confinement fusion and the target physics basis for ignition and gain
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Automated simulation-based design via multi-fidelity active learning and optimisation for laser direct drive implosions
A multi-fidelity active-learning optimizer found a 25 kJ laser-direct-drive target whose hydrodynamically scaled 2 MJ simulation burns with about 217x alpha amplification, far more than the 1D-optimized design's 17x.