Fig. 1. The temporal pulse shaping schematic in the SG-II-up high-power laser facility.

Fig. 2. The electrical waveform and temporal waveform in the pulse shaping unit: (a) the electrical waveform set by the AWG; (b) the temporal waveform collected at the front-end system.

Fig. 3. Convolutional neural network model structure.

Fig. 4. Typical simulated waveform data.

Fig. 5. The progression of the loss function for both the training and validation sets.

Fig. 6. The simulated waveforms and corresponding denoising results obtained by the model: (a)–(c), (g)–(i) input waveforms; (d)–(f), (j)–(l) denoised waveforms and ideal waveforms.

Fig. 7. The simulated waveforms and corresponding denoising results obtained by the model: (a) input waveform; (b) output waveform of one model run and the ideal waveform; (c) output waveform of three model runs and the ideal waveform.

Fig. 8. Model performance with different numbers of calculations: (a) RMSE and SNR; (b) errors in the contrast of the waveforms; (c) time of the calculations.

Fig. 9. Comparison of complex input waveforms and denoised waveforms obtained by the model: (a)–(c) input waveforms; (d)–(f) denoised waveforms and ideal waveforms.

Fig. 10. The temporal waveforms of the experiment and corresponding denoising results obtained by the model: (a)–(c) input waveforms; (d)–(f) denoised waveforms.

Fig. 11. Different types of temporal waveforms of the experiment and corresponding denoising results obtained by the model: (a)–(c) input waveforms; (d)–(f) denoised waveforms.

Fig. 12. Electric waveforms with the same contrast set by the AWG.

Fig. 13. The temporal waveforms of the experiment and corresponding denoised results obtained by the model: (a) input waveforms; (b) denoised waveforms; (c) contrast of five denoised temporal waveforms.