摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-研究人员详细介绍了机器学习的新数据。根据来自法国图卢兹的新sRx记者的新闻报道，研究表明，“在竞争日益激烈和数字化的工业环境中，结构优化不仅是降低成本的关键，也是减少自然资源消耗的关键。为此，历史上出现了基于当时可用工具的不同方法。”我们的新闻编辑从土罗大学的研究中获得了一句话，“随着人工智能和机器学习的概念的兴起，”革命性的想法正在出现，允许在创纪录的时间内对结构进行最优尺寸标注。本文提出了使用变分自动求解器和混合变量求解器作为结构优化和材料选择的建议。本文在前人研究的基础上，提出了三个方向：(1)引入更多的材料属性，与环境考虑特别相关；(2)更详细地分析VAE的各个方面，如潜在空间的维度；以及(3)选择最佳候选者的两步混合方法：用VAE进行初步过滤，通过混合变量模型进行最终设计"。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting originating from Toulouse, France, by New sRx correspondents, research stated, “In an increasingly competitive and digital industrial environment, the optimization of structures is a key point not only to reduce costs but also to reduce the consumption of natural resources. To this end, different approaches have emerged throughout history based on the tools av ailable at the time.” Our news editors obtained a quote from the research from the University of Toulo use, “With the current rise of artificial intelligence and the concept of machin e learning, revolutionary ideas are emerging that allow an optimal dimensioning of structures in record time. This work presents the use of variational autoenco ders and mixed-variable solvers as a proposal for structural optimization and ma terial selection. It has expanded upon previous research by advancing in three d irections: (1) incorporating more material attributes, particularly relevant for environmental considerations; (2) analyzing in more detail aspects of VAEs such as the dimensionality of the latent space; and (3) a two-step hybrid approach t o select the optimal candidate: preliminary filtering with VAE and final design via mixed-variable model.”
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