Robotics & Machine Learning Daily News2024,Issue(Jun.18) :80-80.

Nanjing Tech University Researcher Provides New Insights into Machine Learning ( Deep Learning Accelerated Design of Bezier Curve-Based Cellular Metamaterials wi th Target Properties)

南京理工大学研究员为机器学习提供了新的见解(基于Bezier曲线的具有目标特性的细胞超材料的深度学习加速设计)

Robotics & Machine Learning Daily News2024,Issue(Jun.18) :80-80.

Nanjing Tech University Researcher Provides New Insights into Machine Learning ( Deep Learning Accelerated Design of Bezier Curve-Based Cellular Metamaterials wi th Target Properties)

南京理工大学研究员为机器学习提供了新的见解(基于Bezier曲线的具有目标特性的细胞超材料的深度学习加速设计)

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摘要

由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx记者从中国江苏发回的新闻报道,研究表明:“机器学习已经引起了人们对机械超材料设计领域的极大兴趣。这些超材料的独特性能源于微观结构,而不是组成材料本身。”本研究的资助者包括中国国家重点研究开发项目。新闻编辑从南京理工大学的研究中得到一句话:“在这种背景下,”我们介绍了一种新的数据驱动方法,用于设计具有特定目标特性的正交异性细胞超材料。我们的方法利用Bezier曲线框架,在TS中战略性地放置控制点。机器学习模型利用这些控制点的位置来获得所需的材料特性。这个过程包括两个主要步骤。我们建立了一个基于给定设计的材料性能预测正向模型,然后构造了一个以材料性能为输入,产生相应的设计参数作为输出的反向模型,结果表明,使用Bezier曲线基D策略生成的数据具有广泛的弹性分布,用设计参数描述几何形状,而不是用像素图形描述几何形状。提高了网络的传输效率。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Jiangsu, People's Republic of China, by NewsRx correspondents, research stated, "Machine learning has sparked significant interest in the realm of designing mechanical metamater ials. These metamaterials derive their unique properties from microstructures ra ther than the constituent materials themselves." Funders for this research include National Key Research And Development Program of China. The news editors obtained a quote from the research from Nanjing Tech University : "In this context, we introduce a novel data-driven approach for the design of an orthotropic cellular metamaterials with specific target properties. Our metho dology leverages a Bezier curve framework with strategically placed control poin ts. A machine learning model harnesses the positions of these control points to achieve the desired material properties. This process consists of two main steps . Initially, we establish a forward model capable of predicting material propert ies based on given designs. Then, we construct an inverse model that takes mater ial properties as inputs and produces corresponding design parameters as outputs . Our results demonstrate that the dataset generated using the Bezier curve-base d strategy shows a wide range of elastic distributions. Describing the geometry in terms of design parameters, rather than pixel-based figures, enhances the tra ining efficiency of the networks."

Key words

Nanjing Tech University/Jiangsu/People 's Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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