Control and Information Systems for the National Ignition Facility

CNN-based neural network model for amplified laser pulse temporal shape prediction with dynamic requirement in high-power laser facility

The temporal shape of laser pulses is one of the essential performances in the inertial confinement fusion (ICF) facility. Due to the complexity and instability of the laser propagation system, it is hard to predict the pulse shapes precisely by pure analytic methods based on the physical model [Frantz-Nodvik (F-N) equation]. Here, we present a data-driven model based on a convolutional neural network (CNN) for precise prediction. The neural network model introduces sixteen parameters neglected in the F-N equation based models to expand the representation dimension. The sensitivity analysis of the experimental results confirms that these parameters have different degrees of influence on the temporal output shapes and cannot be ignored. The network characterizes the whole physical process with commonality and specificity features to improve the description ability. The prediction accuracy evaluated by a root mean square of the proposed model is 7.93%, which is better compared to three optimized physical models. This study explores a nonanalytic methodology of combining prior physical knowledge with data-driven models to map the complex physical process by numerical models, which has strong representation capability and great potential to model other measurable processes in physical science.

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

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.