Active learning–augmented end-to-end modeling toward fast inverse design in chirped pulse amplification

Helin Jiang; Guoqing Pu; Xinyi Ma; Weisheng Hu; Lilin Yi

doi:10.1117/1.APN.4.4.046016

Advanced Photonics Nexus, Volume. 4, Issue 4, 046016(2025)

Active learning–augmented end-to-end modeling toward fast inverse design in chirped pulse amplification

Helin Jiang, Guoqing Pu^*, Xinyi Ma, Weisheng Hu, and Lilin Yi^*

Shanghai Jiao Tong University, School of Information Science and Electronic Engineering, State Key Laboratory of Photonics and Communications, Shanghai, China

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Fig. 1. Concise illustration of the CPA simulation system.

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Fig. 2. (a) Proposed E2E model for simulating the CPA system. (b) Workflow of the active learning strategy.

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Fig. 3. Output pulses of the E2E-AL model and SSFM-RK4. (a)–(c) Comparison of the intensity and phase in the spectral domain. (d)–(f) Comparison of the intensity and phase in the temporal domain. (g)–(i) Comparison of the intensity and phase in the temporal domain after fine-tuning compression. (j)–(l) Comparison of the intensity and phase in the spectral domain after fine-tuning compression.

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Fig. 4. Intensity NRMSE and time consumption comparison between the SSFM-RK4 and E2E-AL model.

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Fig. 5. Results of the active learning. The NRMSE of the E2E (red solid line) and E2E-AL (blue solid line) models under different train samples. The active learning strategy reduces the number of training samples as indicated by the yellow dashed line.

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Fig. 6. Inverse design workflow of the CPA system.

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Fig. 7. Inverse design comparison between the SSFM-RK4 and E2E-AL under different target pulses. (a) and (b) Single-objective inverse design of the pulse duration. (c) and (d) Multi-objective inverse design of the pulse duration and energy. (a) and (c) Temporal intensity comparison among the target pulse, the result of the SSFM-RK4 and GA, and the result of the E2E-AL and GA. (b) and (d) Error comparison between the most matching pulse of the SSFM-RK4 and GA and the E2E-AL and GA with the target pulse in each generation.

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Table 1. Parameters of the CPA system.

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Table 1. Parameters of the CPA system.


Type	Property	Value
System	Temporal window	4 ns
Temporal resolution	20 fs
Seed pulse	Central wavelength	1030 nm
Duration	100 to 500 fs
Energy	0.5 to 1.5 nJ
Chirped coefficients	−2 to 2
CFBG	$β_{2}$	19.175 to $19.739 {ps}^{2}$
$β_{3}$	−0.1565 to $- 0.1875 {ps}^{3}$
YDF 1	Core diameter	$6 μ m$
Cladding diameter	$125 μ m$
Length	1 m
Pump 1	Gain	10 to 15 dB
YDF 2	Core diameter	$14 μ m$
Cladding diameter	$125 μ m$
Length	1 m
Pump 2	Gain	10 to 14 dB
NKT rod fiber	Core diameter	$85 μ m$
Cladding diameter	$260 μ m$
Length	0.8 m
Pump 3	Gain	10 to 14 dB
Grating pairs	Grating distance	0.358 m
Grating period	1/1600 mm
Angle of incidence	55.558 deg

Table 2. Comparison between the SSFM-RK4 and E2E-AL model.

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Table 2. Comparison between the SSFM-RK4 and E2E-AL model.


	SSFM-RK4	SSFM-RK4	SSFM-RK4	E2E-AL
Step size	1 mm	6 mm	16 mm	N/A
Simulation time^a	198 s	32 s	13 s	0.015 s
Memory^b	9509 MB	4885 MB	1054 MB	62 MB
Intensity NRMSE	N/A	0.00145	0.02232	0.00108
Phase NRMSE	N/A	0.00104	0.12088	0.00199
Energy MAPE	N/A	0.47%	7.24%	0.51%
Duration MAPE	N/A	0	0	0
Training time	N/A	N/A	N/A	11,254 s
Training sample	N/A	N/A	N/A	45,000

Table 3. Time consumption comparison of the CPA system inverse design.^a
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Table 3. Time consumption comparison of the CPA system inverse design.^a
E2E-AL and GA SSFM-RK4 and GA
Model time (s) 0.0025 34.8068
Fine-tuning compression time (s) 1.3189 1.4543
Total time (s) 1.3215 36.2611

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Helin Jiang, Guoqing Pu, Xinyi Ma, Weisheng Hu, Lilin Yi, "Active learning–augmented end-to-end modeling toward fast inverse design in chirped pulse amplification," Adv. Photon. Nexus 4, 046016 (2025)

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