Embedding AI into laser pulse shaping closed-loop control

Luo, Jun; Tian, Zhiyu; Li, Li; Ni, Zhigao; Xie, Xingquan; Zhou, Xiaowei

doi:10.1016/j.fusengdes.2023.113888

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The closed-loop control method used for pulse shaping on a current high-power laser facility is an artificial intelligence-assisted (AIA) method, as described in detail in reference [14]. The core of the AIA method is to assign an initial value to the set voltage of AWG during the closed-loop process through a trained UNet.…”

Section: Comparison Resultsmentioning

confidence: 99%

See 1 more Smart Citation

UNet-Based Framework for Predicting the Waveform of Laser Pulses of the Front-End System in a Current High-Power Laser Facility

Liao,

Huang,

Geng

et al. 2023

Photonics

View full text Add to dashboard Cite

Performing data mining on large waveform datasets of a high-power laser facility is an important way to achieve precise regulation of a device. However, there are currently issues with missing values, noise, and inconsistency in this database of measuring pulse waveform in a current high-power laser facility. In this paper, a UNet of a series residual module is presented to predict the pulse waveform of a front-end chained system in a current high-power laser facility. The designed network is trained on grouped sequences formed by experimentally measuring pulse waveforms of a high-power laser facility. The strategies of relay output and relay loss are employed in training in order to enable the network to predict two kinds of pulse waveforms simultaneously. The trained network achieved an RMSE of 3.38% on the testing set of measuring pulse waveform at a frequency of 1 Hz, and an RMSE of 0.84% on the testing set of setting the voltage of the Arbitrary Waveform Generator (AWG). These results indicate that this method can accurately fill in paired missing values in the waveform database of a high-power laser facility. The main advantage of this method is that it can quickly couple operational parameters for prediction, and this method can be applied to predicting laser performance, cleaning one-dimensional sequences, and maintaining a waveform database.

show abstract

Section: Comparison Resultsmentioning

confidence: 99%

“…In 2023, Jun Luo et al proposed an artificial intelligence-assisted method to improve the efficiency of the traditional closed-loop control process. By training the U-Net model to predict the initial AWG pulse waveform in the closed-loop process, the closed-loop control process of pulse shaping is greatly accelerated [14].…”

Section: Introductionmentioning

confidence: 99%

UNet-Based Framework for Predicting the Waveform of Laser Pulses of the Front-End System in a Current High-Power Laser Facility

Liao,

Huang,

Geng

et al. 2023

Photonics

View full text Add to dashboard Cite

show abstract

Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities

Chen,

Lu,

Fan

et al. 2024

High Pow Laser Sci Eng

View full text Add to dashboard Cite

For the pulse shaping system of the SG-II-up facility, we propose a U-shaped convolutional neural network that integrates multi-scale feature extraction capabilities, an attention mechanism and long short-term memory units, which effectively facilitates real-time denoising of diverse shaping pulses. We train the model using simulated datasets and evaluate it on both the simulated and experimental temporal waveforms. During the evaluation of simulated waveforms, we achieve high-precision denoising, resulting in great performance for temporal waveforms with frequency modulation-to-amplitude modulation conversion (FM-to-AM) exceeding 50%, exceedingly high contrast of over 300:1 and multi-step structures. The errors are less than 1% for both root mean square error and contrast, and there is a remarkable improvement in the signal-to-noise ratio by over 50%. During the evaluation of experimental waveforms, the model can obtain different denoised waveforms with contrast greater than 200:1. The stability of the model is verified using temporal waveforms with identical pulse widths and contrast, ensuring that while achieving smooth temporal profiles, the intricate details of the signals are preserved. The results demonstrate that the denoising model, trained utilizing the simulation dataset, is capable of efficiently processing complex temporal waveforms in real-time for experiments and mitigating the influence of electronic noise and FM-to-AM on the time–power curve.

show abstract

Embedding AI into laser pulse shaping closed-loop control

Cited by 2 publications

References 12 publications

UNet-Based Framework for Predicting the Waveform of Laser Pulses of the Front-End System in a Current High-Power Laser Facility

UNet-Based Framework for Predicting the Waveform of Laser Pulses of the Front-End System in a Current High-Power Laser Facility

Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities

Contact Info

Product

Resources

About