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August 9, 2026APL Machine LearningOpen Access

Demonstration of machine-learning-based control for femtosecond-level pulse-shaping applications

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Authors

JNJ. H. NicolauSBS. M. BuczekGCGilbert Collins

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Overview

Randomized trial demonstrates improved pulse shaping in ultrafast lasers, suggesting advances in laser technology applications.

Key Points

  • The aim is to enhance femtosecond pulse shaping in high-power laser systems using machine learning techniques.
  • Trained multiple machine-learning models on experimental data to capture nonlinear amplification characteristics.
  • Utilized an acousto-optic programmable dispersive filter for spectral phase control while stabilizing spectral intensity.
  • Compared performance across different algorithms and training dataset sizes to optimize pulse shaping.
  • Demonstrated the production of custom-defined pulse shapes at TW-level peak powers.
  • Achieved effective control of dispersion coefficients, marking progress towards arbitrary pulse shaping.
  • Highlighted differences in algorithm performance across various configurations and datasets.

Cite This Study

Nicolau et al. (2026) studied this question.

synapsesocial.com/papers/6a782d5f2e1896536c84053dhttps://doi.org/10.1063/5.0328951
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