Maximizing supercontinuum bandwidths in gas-filled hollow-core fibers using artificial neural networks

نویسندگان

چکیده

Machine learning has been used to accelerate studies in the dynamics of optical pulses. In this study, we use machine investigate optimal design supercontinuum-generating hollow-core antiresonant fibers (HC-ARFs) pressurized and filled with methane. Artificial neural networks (ANNs) are trained replace numerical solvers, simulation fibers, provide a more rapid fiber procedure. We first an analytical model approximate dispersion loss methane-filled silica HC-ARF. This approximation is by generalized unidirectional pulse propagation equation solver simulate generate training data for our ANNs varying parameters including pump center wavelength, radius, length, cladding strut thickness, gas pressure. evaluate performance different spectral-predicting ANN architectures along custom function search full parameter space. Subsequently, regions predicted high identified, these high-performance HC-ARF designs further optimized supercontinuum generation at target bandwidths. also extend effort maximizing total spectral energy outside input wavelength integrating global optimization technique design.

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ژورنال

عنوان ژورنال: Journal of Applied Physics

سال: 2023

ISSN: ['1089-7550', '0021-8979', '1520-8850']

DOI: https://doi.org/10.1063/5.0148238