Unsupervised Learning of Particles Dispersion

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.

Automated summary

This paper discusses the use of unsupervised learning to classify particle-like dispersion and its relevance to various applications such as virus transmission and atmospheric pollution. The RUN-ICON algorithm of unsupervised learning is applied to classify particle spread with higher confidence and lower uncertainty compared to other algorithms, even in the presence of noise. The combination of unsupervised learning and the RUN-ICON algorithm provides a tool for studying particle dynamics and their impact on air quality, health, and climate.