Opportunities for large language models and discourse in engineering design

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jan Göpfert , Jann M. Weinand , Patrick Kuckertz , Detlef Stolten
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引用次数: 0

Abstract

In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. As an example, we present a design discourse on the optimization of wind turbine blades. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

Abstract Image

大型语言模型和话语在工程设计中的机遇
近年来,大型语言模型在自然语言处理的各种基准测试中取得了突破性进展,其性能还在不断提高。最近,大型语言模型的进步引起了自然语言处理界以外的关注,并可能对日常生活产生巨大影响。在本文中,我们提出了一个问题:大型语言模型和其他基础模型将如何塑造未来的产品开发流程?我们通过总结自然语言处理的最新进展和工程设计过程中信息技术的应用,为读者提供了这一主题的概览。我们认为,话语应被视为工程设计流程的核心,因此应该用数字工具来表示。在此基础上,我们描述了大型语言模型等基础模型如何通过将其中涉及创造力和推理的部分自动化来促进设计话语,而这些部分以前都是由人类来完成的。我们介绍了如何将模拟、实验、拓扑优化和其他流程步骤整合到机器可执行的、以话语为中心的设计流程中。作为一个例子,我们介绍了风力涡轮机叶片优化的设计论述。最后,我们概述了实施概念化框架所需的未来研究。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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