Large Language Model-assisted Clustering and Concept Identification of Engineering Design Data

Recent advances in Large Language Models (LLM) open up opportunities for users to interact with domain specific knowledge and execute (semi-)professional tasks in a dialog fashion. Without profound knowledge in data science and programming languages, basic statistics and further detailed analyses can be conducted intuitively through natural language prompts. Accessing common data science methods, LLMs can assist users in visualizing, interpreting, and analyzing large and complex data sets. In this paper, we study the potential of ChatGPT from the perspective of different user expertise levels to process and interpret existing data sets which originate from engineering optimization tasks with varying complexity—– a proportional-integral-controller configuration, an aerodynamic design optimization, and an energy management task—–and use it to cluster the data into meaningful groups. Furthermore, we provide the LLM with a recently developed concept identification metric and assess its capability to use the metric for an allocation of the samples into technically reasonable concepts. On one hand, the formulation of conceptual machine learning pipelines, as well as using well-established methods, such as typical clustering algorithms, are conducted seamlessly by ChatGPT for the given tasks. On the other hand, it is observed that automatically constructing a concept identification algorithm from its mathematical formulation using ChatGPT still requires supervision and support from a domain expert.