Bibliometric review demonstrates millisecond-scale recovery via machine learning and electro-optic devices, highlighting pathways toward autonomous ultrafast lasers in harsh environments.
Key Points
Evaluate algorithmic and hardware co-development strategies for intelligent polarization control to stabilize pulse dynamics in ultrafast fiber lasers against environmental perturbations.
Conducted a systematic bibliometric review categorizing algorithmic control strategies into traversal, optimization, and machine learning methods.
Analyzed hardware operating mechanisms across stress-induced, liquid-crystal, electro-optic, and magneto-optic polarization controllers.
Identified a paradigm shift combining high-speed electro-optic controllers with machine learning to achieve laser mode-locking and perturbation recovery within millisecond timescales.
Outlined future technological transitions requiring physics-driven artificial intelligence paired with field-programmable gate array (FPGA) architectures for autonomous, self-recovering laser operation.