Study Data from Western Sydney University Update Knowledge of Machine Learning [Pharmaceutical Aerosol Transport In Airways: a Combined Machine Learning (Ml) an d Discrete Element Model (Dem) Approach]

西悉尼大学的研究数据更新机器学习知识[药物气溶胶在气道中的传输：一种组合机器学习（Ml）和三维离散元模型（Dem）方法]

Robotics & Machine Learning Daily News2024，Issue(Nov.29) ：89-89.

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Study Data from Western Sydney University Update Knowledge of Machine Learning [Pharmaceutical Aerosol Transport In Airways: a Combined Machine Learning (Ml) an d Discrete Element Model (Dem) Approach]

西悉尼大学的研究数据更新机器学习知识[药物气溶胶在气道中的传输：一种组合机器学习（Ml）和三维离散元模型（Dem）方法]

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据新闻报道由News Rx编辑撰写的澳大利亚彭里斯的研究报告指出，“连续和离散相相互作用”会显著影响吸入颗粒的运输行为。连续统的精确分析离散相相互作用需要计算流体力学(CFD)和离散元方法(DEM)模拟，计算成本很高。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reporting outof Penrith, Australia, by News Rx editors, research stated, “The continuum and discrete phase interactioncould significantly affect the inhaled particle’s transport behaviour. The accurate a nalysis of continuumand discrete phase interaction needs computational fluid dy namics (CFD) and Discrete Element Method(DEM) simulation, which is computationa lly expensive.”

Key words

Penrith/Australia/Australia and New Ze aland/Computational Fluid Dynamics/Cyborgs/Emerging Technologies/Fluid Mecha nics/Machine Learning/Western Sydney University

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出版年

2024

Robotics & Machine Learning Daily News

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