Abstract
We present a general method to determine the parameters of nonlinear pulse shaping systems based on pulse propagation in a normally dispersive fiber that are required to achieve the generation of pulses with various specified temporal properties. The nonlinear shaping process is reduced to a numerical optimization problem over a three-dimensional space, where the intersections of different surfaces provide the means to quickly identify the sets of parameters of interest. We also show that the implementation of a machine-learning strategy can efficiently address the multi-parameter optimization problem being studied.
Original language | English |
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Pages (from-to) | 306-312 |
Journal | Optical Fiber Technology |
Volume | 45 |
Early online date | 23 Aug 2018 |
DOIs | |
Publication status | Published - Nov 2018 |
Keywords
- Nonlinear shaping, Machine learning, Nonlinear fiber optics