Energy prediction with physics-guided neural networks for high-power laser facility

The energy accuracy of laser beams is an essential property of inertial confinement fusion (ICF). However, the energy gain is difficult to be predicted and controlled precisely due to the dramatically-increasing complexity of huge optical systems. Artificial neural network is a numerical algorithm with valuable flexibility that maps inputs to output values, which provides an approach to figure out this issue. In the study, a novel method combining deep neural networks and the Frantz- Nodvik equations is proposed to predict the output energy of the main amplifier in the high-power ICF laser system. To improve the prediction performance, the artificial neural network counts in more related factors that are neglected in traditional configurations. Dynamic state parameters describing amplification capacity are output by neural network and constrained by physical prior knowledge. The experimental results show that the proposed method provides a more accurate prediction of output energy than the conventional fitting approaches, from 6.5% to 4.2% on relative deviation. The study investigates the methodology of combining neural networks with physical models to reproduce a complex energy gain process and to represent a nonlinear unresolvable model, which can be exploited to aid model development of other measurable processes in physical science.