Unsupervised Learning of Particles Dispersion

Nicholas Christakis, Dimitris Drikakis

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish
    Article number3637
    JournalMathematics
    Volume11
    Issue number17
    DOIs
    Publication statusPublished - Sept 2023

    Keywords

    • air quality
    • artificial intelligence
    • atmospheric pollution
    • machine learning
    • particles dispersion
    • unsupervised learning
    • virus transmission

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