Deep learning and high harmonic generation
نویسندگان
چکیده
Using machine learning, we explore the utility of various deep neural networks (NN) when applied to high harmonic generation (HHG) scenarios. First, train NNs predict time-dependent dipole and spectra HHG emission from reduced-dimensionality models di- triatomic systems based on sets randomly generated parameters (laser pulse intensity, internuclear distance, molecular orientation). These networks, once trained, are useful tools rapidly generate our systems. Similarly, have trained solve inverse problem - determine or acceleration data. types could then be used as spectroscopic invert in order recover underlying physical a system. Next, demonstrate that transfer learning can expand range applicability with only small number new test cases added training sets. Finally, classify molecules by type: triatomic, symmetric asymmetric, wherein even rely fairly simple fully connected networks. With outlooks toward experimental data, these NN topologies offer novel set incorporated into experiments.
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ژورنال
عنوان ژورنال: Canadian Journal of Physics
سال: 2023
ISSN: ['0008-4204', '1208-6045']
DOI: https://doi.org/10.1139/cjp-2022-0115