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Unsupervised Learning of Particles Dispersion

Author

Listed:
  • Nicholas Christakis

    (Institute for Advanced Modelling and Simulation, University of Nicosia, Nicosia CY-2417, Cyprus
    Laboratory of Applied Mathematics, University of Crete, GR-70013 Heraklion, Greece)

  • Dimitris Drikakis

    (Institute for Advanced Modelling and Simulation, University of Nicosia, Nicosia CY-2417, Cyprus)

Abstract

This paper discusses using unsupervised learning in classifying particle-like dispersion. The problem is relevant to various applications, including virus transmission and atmospheric pollution. The Reduce Uncertainty and Increase Confidence (RUN-ICON) algorithm of unsupervised learning is applied to particle spread classification. The algorithm classifies the particles with higher confidence and lower uncertainty than other algorithms. The algorithm’s efficiency remains high also when noise is added to the system. Applying unsupervised learning in conjunction with the RUN-ICON algorithm provides a tool for studying particles’ dynamics and their impact on air quality, health, and climate.

Suggested Citation

  • Nicholas Christakis & Dimitris Drikakis, 2023. "Unsupervised Learning of Particles Dispersion," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3637-:d:1223086
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    References listed on IDEAS

    as
    1. Linwei Hu & Jie Chen & Joel Vaughan & Soroush Aramideh & Hanyu Yang & Kelly Wang & Agus Sudjianto & Vijayan N. Nair, 2021. "Supervised Machine Learning Techniques: An Overview with Applications to Banking," International Statistical Review, International Statistical Institute, vol. 89(3), pages 573-604, December.
    2. Nicholas Christakis & Dimitris Drikakis, 2023. "Reducing Uncertainty and Increasing Confidence in Unsupervised Learning," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    Full references (including those not matched with items on IDEAS)

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