Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning
Frequency synthesis and spectro-temporal control of optical wavepackets are central to ultrafast science, with supercontinuum (SC) generation standing as one remarkable example. Through passive manipulation, femtosecond pulses from nanojoule-level lasers can be transformed into octave-spanning spect...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Ultrafast Science |
| Online Access: | https://spj.science.org/doi/10.34133/ultrafastscience.0107 |
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| author | Shilong Liu Stéphane Virally Gabriel Demontigny Patrick Cusson Denis V. Seletskiy |
| author_facet | Shilong Liu Stéphane Virally Gabriel Demontigny Patrick Cusson Denis V. Seletskiy |
| author_sort | Shilong Liu |
| collection | DOAJ |
| description | Frequency synthesis and spectro-temporal control of optical wavepackets are central to ultrafast science, with supercontinuum (SC) generation standing as one remarkable example. Through passive manipulation, femtosecond pulses from nanojoule-level lasers can be transformed into octave-spanning spectra, supporting few-cycle pulse outputs when coupled with external pulse compressors. While strategies such as machine learning have been applied to control the SC’s central wavelength and bandwidth, their success has been limited by the nonlinearities and strong sensitivity to measurement noise. Here, we propose and demonstrate how a physics-embedded convolutional neural network that embeds spectro-temporal correlations can circumvent such challenges, resulting in faster convergence and reduced noise sensitivity. This innovative approach enables on-demand control over spectro-temporal features of SC, achieving few-cycle pulse shaping without external compressors. This approach heralds a new era of arbitrary spectro-temporal light state engineering, with implications for ultrafast photonics, photonic neuromorphic computation, and artificial intelligence-driven optical systems. |
| format | Article |
| id | doaj-art-19748e938bb94328a55fedb85daa1dc7 |
| institution | Kabale University |
| issn | 2765-8791 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Ultrafast Science |
| spelling | doaj-art-19748e938bb94328a55fedb85daa1dc72025-08-20T04:01:02ZengAmerican Association for the Advancement of Science (AAAS)Ultrafast Science2765-87912025-01-01510.34133/ultrafastscience.0107Engineering Spectro-Temporal Light States with Physics-Embedded Deep LearningShilong Liu0Stéphane Virally1Gabriel Demontigny2Patrick Cusson3Denis V. Seletskiy4femtoQ Lab, Department of Engineering Physics, Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada.femtoQ Lab, Department of Engineering Physics, Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada.femtoQ Lab, Department of Engineering Physics, Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada.femtoQ Lab, Department of Engineering Physics, Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada.femtoQ Lab, Department of Engineering Physics, Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada.Frequency synthesis and spectro-temporal control of optical wavepackets are central to ultrafast science, with supercontinuum (SC) generation standing as one remarkable example. Through passive manipulation, femtosecond pulses from nanojoule-level lasers can be transformed into octave-spanning spectra, supporting few-cycle pulse outputs when coupled with external pulse compressors. While strategies such as machine learning have been applied to control the SC’s central wavelength and bandwidth, their success has been limited by the nonlinearities and strong sensitivity to measurement noise. Here, we propose and demonstrate how a physics-embedded convolutional neural network that embeds spectro-temporal correlations can circumvent such challenges, resulting in faster convergence and reduced noise sensitivity. This innovative approach enables on-demand control over spectro-temporal features of SC, achieving few-cycle pulse shaping without external compressors. This approach heralds a new era of arbitrary spectro-temporal light state engineering, with implications for ultrafast photonics, photonic neuromorphic computation, and artificial intelligence-driven optical systems.https://spj.science.org/doi/10.34133/ultrafastscience.0107 |
| spellingShingle | Shilong Liu Stéphane Virally Gabriel Demontigny Patrick Cusson Denis V. Seletskiy Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning Ultrafast Science |
| title | Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning |
| title_full | Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning |
| title_fullStr | Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning |
| title_full_unstemmed | Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning |
| title_short | Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning |
| title_sort | engineering spectro temporal light states with physics embedded deep learning |
| url | https://spj.science.org/doi/10.34133/ultrafastscience.0107 |
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