Abstract The task of design feature transcription, or encoding the functional requirements and design parameters of a design, requires representing design data such that a machine can comprehend. Natural language processing, powered by deep neural networks trained on massive corpora of textual data, can map language into distributed vector representation space that machines can understand and retrieve. This work outlines how language models can be used to enhance early-stage design by separating the functional and physical domains, abstracting key functional requirements, and analysing systems to provide metrics for good design decision making, to facilitate a framework for hybrid intelligence.

中文翻译：

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设计转录：基于深度学习的设计特征表示

摘要 设计特征转录或编码设计的功能要求和设计参数的任务需要表示设计数据，以便机器可以理解。自然语言处理由在大量文本数据语料库上训练的深度神经网络提供支持，可以将语言映射到机器可以理解和检索的分布式向量表示空间。这项工作概述了如何使用语言模型通过分离功能域和物理域、抽象关键功能需求和分析系统来为良好的设计决策提供指标来增强早期设计，从而促进混合智能框架。