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Femtosecond pulse compression using a neural-network algorithm
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DOI：10.1364/OL.43.005166.png)
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Abstract
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A key requirement for femtosecond spectroscopy measurements is to compress the laser pulse to its transform-limited duration. In particular, for few-cycle laser pulses, the compression process is time-consuming using conventional algorithms that converge statistically. Here we show that machine learning can accelerate the process of pulse compression: we have developed an adaptive neural-network algorithm to control a deformable-mirror-based pulse shaper that converges 100x faster than a standard evolutionary algorithm. (C) 2018 Optical Society of America
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