摘要
由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在已经可用。根据Ne wsRx记者在德国卡尔斯鲁厄的新闻报道，研究表明：“在本文中，我们结合基于快速傅立叶变换的计算方法和有监督的机器学习策略来发现剪切稀化流体悬浮液各向异性有效粘度的模型。使用基于快速傅立叶变换的计算方法，我们研究了纤维取向状态和施加的宏观剪切速率张量对工程过程中广泛剪切速率范围内有效粘度的影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating in Karlsruhe, Germany, by Ne wsRx journalists, research stated, “In this article, we combine a Fast Fourier T ransform based computational approach and a supervised machine learning strategy to discover models for the anisotropic effective viscosity of shear-thinning fi ber suspensions. Using the Fast Fourier Transform based computational approach, we study the effects of the fiber orientation state and the imposed macroscopic shear rate tensor on the effective viscosity for a broad range of shear rates of engineering process interest.”
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