利用无监督学习和高斯混合物模型研究粒子弥散统计

IF 4.1 2区 工程技术 Q1 MECHANICS Physics of Fluids Pub Date : 2024-09-10 DOI:10.1063/5.0229111
Nicholas Christakis, Dimitris Drikakis
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引用次数: 0

摘要

了解颗粒在封闭空间中的扩散情况对于控制传染病的传播至关重要。本研究引入了一种创新方法,将无监督学习算法与高斯混合物模型相结合,分析咳嗽者唾液飞沫的行为。该算法能有效地对数据进行聚类,而高斯混合物模型则能捕捉这些聚类的分布,从而揭示潜在的亚群和颗粒分散的变化。利用计算流体动力学模拟数据,这种综合方法为粒子动力学提供了一个强大的、数据驱动的视角,揭示了以前无法实现的复杂模式和概率分布。这种综合方法大大提高了预测的准确性和可解释性,为防止病毒在室内环境传播的公共卫生策略提供了宝贵的见解。这项研究具有深远的现实意义,因为它展示了先进的无监督学习技术在应对复杂的生物医学和工程挑战方面的潜力,并强调了将复杂算法与统计模型结合起来进行综合数据分析的重要性。这些发现对公共卫生战略的潜在影响是巨大的,凸显了这项研究与现实世界应用的相关性。
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On particle dispersion statistics using unsupervised learning and Gaussian mixture models
Understanding the dispersion of particles in enclosed spaces is crucial for controlling the spread of infectious diseases. This study introduces an innovative approach that combines an unsupervised learning algorithm with a Gaussian mixture model to analyze the behavior of saliva droplets emitted from a coughing individual. The algorithm effectively clusters data, while the Gaussian mixture model captures the distribution of these clusters, revealing underlying sub-populations and variations in particle dispersion. Using computational fluid dynamics simulation data, this integrated method offers a robust, data-driven perspective on particle dynamics, unveiling intricate patterns and probabilistic distributions previously unattainable. The combined approach significantly enhances the accuracy and interpretability of predictions, providing valuable insights for public health strategies to prevent virus transmission in indoor environments. The practical implications of this study are profound, as it demonstrates the potential of advanced unsupervised learning techniques in addressing complex biomedical and engineering challenges and underscores the importance of coupling sophisticated algorithms with statistical models for comprehensive data analysis. The potential impact of these findings on public health strategies is significant, highlighting the relevance of this research to real-world applications.
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来源期刊
Physics of Fluids
Physics of Fluids 物理-力学
CiteScore
6.50
自引率
41.30%
发文量
2063
审稿时长
2.6 months
期刊介绍: Physics of Fluids (PoF) is a preeminent journal devoted to publishing original theoretical, computational, and experimental contributions to the understanding of the dynamics of gases, liquids, and complex or multiphase fluids. Topics published in PoF are diverse and reflect the most important subjects in fluid dynamics, including, but not limited to: -Acoustics -Aerospace and aeronautical flow -Astrophysical flow -Biofluid mechanics -Cavitation and cavitating flows -Combustion flows -Complex fluids -Compressible flow -Computational fluid dynamics -Contact lines -Continuum mechanics -Convection -Cryogenic flow -Droplets -Electrical and magnetic effects in fluid flow -Foam, bubble, and film mechanics -Flow control -Flow instability and transition -Flow orientation and anisotropy -Flows with other transport phenomena -Flows with complex boundary conditions -Flow visualization -Fluid mechanics -Fluid physical properties -Fluid–structure interactions -Free surface flows -Geophysical flow -Interfacial flow -Knudsen flow -Laminar flow -Liquid crystals -Mathematics of fluids -Micro- and nanofluid mechanics -Mixing -Molecular theory -Nanofluidics -Particulate, multiphase, and granular flow -Processing flows -Relativistic fluid mechanics -Rotating flows -Shock wave phenomena -Soft matter -Stratified flows -Supercritical fluids -Superfluidity -Thermodynamics of flow systems -Transonic flow -Turbulent flow -Viscous and non-Newtonian flow -Viscoelasticity -Vortex dynamics -Waves
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