摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-调查人员讨论机器学习的新发现。根据NewsRx编辑在英国拉夫堡发表的新闻报道，研究表明：“为了了解纤维增强塑料在平面外载荷下的损伤形态，工程师和设计师主要依靠实验研究和数值模型，这在资源和计算要求方面可能代价高昂。因此，能够为材料优化提供现成工具的实用预测方法近年来获得了广泛的应用。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting out of Loughborough, United Kingdo m, by NewsRx editors, research stated, “For understanding the damage morphology in fiber-reinforced plastics under out-of-plane loading, engineers/ and designer s mostly rely on experimental investigations and numerical models, which could b e costly in terms of resources and computational requirements. Thus, pragmatic p redictive approaches that can offer ready-to-use tools for material optimization have gained traction in recent years.”
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