Neural network-based energy prediction of high-power laser devices

Tight control of the output energy is required in high-power laser devices. The main amplifier provides the most dominant energy gain, whose output needs to be predicted accurately. However, due to its complex structure and time-varying performance, the prediction results using traditional physical model-fitting methods are biased. In this paper, we propose a physical knowledge-based neural network, with an analytical model as the backbone and multidimensional influencing factors introduced by neural networks as input, to achieve accurate prediction. The method combines the powerful characterization ability of neural networks and the interpretability of physical models, which significantly improves the accuracy by considering the coupling effects of several factors and measurement errors. The relative deviation of the method's prediction results improves 65.9% compared to the traditional physical model and 57.9% compared to the pure neural network. The model provides a correction approach for similar problems of oversimplified physical models and can be exploited to aid model development of other measurable processes in physical science.