Robotics & Machine Learning Daily News2024,Issue(Jun.27) :116-117.

New Machine Learning Findings from University of Washington Described (Efficient Analysis of Composites Manufacturing Using Multi-fidelity Simulation and Probab ilistic Machine Learning)

描述了华盛顿大学机器学习的新发现(使用多保真模拟和概率机器学习对复合材料制造的有效分析)

Robotics & Machine Learning Daily News2024,Issue(Jun.27) :116-117.

New Machine Learning Findings from University of Washington Described (Efficient Analysis of Composites Manufacturing Using Multi-fidelity Simulation and Probab ilistic Machine Learning)

描述了华盛顿大学机器学习的新发现(使用多保真模拟和概率机器学习对复合材料制造的有效分析)

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摘要

机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者在华盛顿州西雅图的新闻报道,研究人员称:“本文介绍了一种有效分析复合材料制造过程和D现象的创新方法。该方法将低保真和高保真模拟方案与有限的实验数据相结合,训练替代机器学习(ML)mo del。”本研究的资助来自东丽复合材料美国公司。-意大利外交和国际合作部长(MAECI)。

Abstract

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 originating in Seattle, Washi ngton, by NewsRx journalists, research stated, “This paper introduces an innovat ive approach for the efficient analysis of composites manufacturing processes an d phenomena. The method combines low- and high-fidelity simulation schemes with limited amounts of experimental data to train surrogate machine learning (ML) mo dels.” Financial support for this research came from Toray Composite Materials America, Inc. - Italian Minister of Foreign Affairs and INTERNATIONAL COOPERATION (MAECI ).

Key words

Seattle/Washington/United States/Nort h and Central America/Cyborgs/Emerging Technologies/Machine Learning/Univers ity of Washington

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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