Laser energy prediction with ensemble neural networks for high-power laser facility

Abstract

The energy accuracy of laser beams is an essential property of the inertial confinement fusion (ICF) facility. However, the energy gain is difficult to control precisely by traditional Frantz-Nodvik equations due to the dramatically-increasing complexity of the huge optical system. A novel method based on ensemble deep neural networks is proposed to predict the laser output energy of the main amplifier. The artificial neural network counts in 39 more related factors that the physical model neglected, and an ensemble method is exploited to obtain robust and stable predictions. The sensitivity of each factor is analyzed by saliency after training to find out the factors which should be controlled strictly. The identification of factor sensitivities reduces relatively unimportant factors, simplifying the neural network model with little effect on the prediction results. The predictive accuracy is benchmarked against the measured energy and the proposed method obtains a relative deviation of 1.59% in prediction, which has a 2.5 times improvement in accuracy over the conventional method.