首页|University of Utah Reports Findings in Machine Learning [Kolm ogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics]
University of Utah Reports Findings in Machine Learning [Kolm ogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Salt Lake City, Utah, by NewsRx editors, research stated, “Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDEs) has garnered much atte ntion in the Computational Science and Engineering (CS&E) world. Th is topic encompasses a broad array of methods and models aimed at solving a sing le or a collection of PDE problems, called multitask learning.”
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