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Application of Artificial Neural Networks in Studying the Dynamic Structure of the Near-Earth Orbital Space

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Russian Physics Journal Aims and scope

A description of the technique for studying the dynamic structure of the near-Earth orbital space using machine learning technology is presented. Artificial neural networks were used to process time series associated with the evolution of resonance characteristics that determine the dynamic structure of the near-Earth region up to 120 thousand km along the semi-major axis. The number of series processed has exceeded half a million, and their manual processing would be time consuming. The results of applying the technique to the analysis of the resonant structure of the selected area of space are presented.

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References

  1. D. S. Krasavin, A. G. Aleksandrova, and I. V. Tomilova, Russ. Phys. J., 63, No. 3, 426–431 (2020).

    Article  Google Scholar 

  2. A. G. Aleksandrova, T. V. Bordovitsyna, and I. N. Chuvashov, Russ. Phys. J., 60, No. 1, 80-89 (2017).

    Article  Google Scholar 

  3. A. G. Aleksandrova, V. A. Avdyushev, N. A. Popandopulo, and T. V. Bordovitsyna, Russ. Phys. J., 64, No. 8, 1566-1575(2021). https://doi.org/10.1007/s11182-021-02491-3

    Article  Google Scholar 

  4. A. G. Aleksandrova, E. V. Blinkova, T. V. Bordovitsyna, N. A. Popandopulo, and I. V. Tomilova, Solar System Research, 55, No. 3, pp. 266–281 (2021).

    Article  ADS  Google Scholar 

  5. A. G. Aleksandrova, T. V. Bordovitsyna, N. A. Popandopulo, et. al., Russ. Phys. J., 63, No. 1, 64-70 (2020).

    Article  ADS  Google Scholar 

  6. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, eBook, (2006).

  7. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, The MIT Press, eBook (2016). URL: http://www.deeplearnmgbook.orgcontents.TOChtml (05.12.2020).

  8. K. V. Vorontsov, Mathematical Teaching Methods by Precedents (Machine Learning). Lecture course. URL: http://www.machinelearning.ru (05.12.2020)

  9. Description of the Torch Library for Python. URL: https:/github.compytorchpytorch (05.12.2020).

  10. Description of the nn Package of the Torch Library for the Python Language. URL: https://pytorch.orgdocsstablenn.html (06.12.2020).

  11. Plas J. Vander, Python for Complex Problems: Data Science and Machine Learning, Piter, St. Petersburg (2018).

  12. S. Rasсhka, Python and Machine Learning [Russian translation], DMK Press, Moscow (2017).

  13. H. Ismail Fawaz, G. Forestier, J. Weber, et al., Data Mining and Knowledge Discovery, 33, Iss. 4, 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1.

    Article  MathSciNet  Google Scholar 

  14. M. T. Hagan, H. B. Demuth, M. Hudson Beale, and O. Jesús, Neural Network Design, 2nd Edition, eBook (2019). URL: https://hagan.okstate.edu/nnd.html (06.12.2019).

  15. D. P. Kingma and M. Welling, An Introduction to Variational Autoencoders [Electronic resource], arXiv.org. 2019. URL: https://arxiv.org/abs/1906.02691.

  16. L. McInnes, J. Healy, and S. Astels, Journal of Open Source Software, The Open Journal, 2(11), 205 (2017). https://doi.org/10.21105/joss.00205.

  17. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), AAAI Press (1996), 226–231 (1996).

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Authors and Affiliations

  1. National Research Tomsk State University, Tomsk, Russia

    D. S. Krasavin, A. G. Aleksandrova & I. V. Tomilova

Authors
  1. D. S. Krasavin
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  2. A. G. Aleksandrova
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  3. I. V. Tomilova
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Correspondence to D. S. Krasavin.

Additional information

Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 10, pp. 38–43, October, 2021.

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Krasavin, D.S., Aleksandrova, A.G. & Tomilova, I.V. Application of Artificial Neural Networks in Studying the Dynamic Structure of the Near-Earth Orbital Space. Russ Phys J 64, 1824–1830 (2022). https://doi.org/10.1007/s11182-022-02528-1

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  • DOI: https://doi.org/10.1007/s11182-022-02528-1

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