Design transcription: Deep learning based design feature representation

Journal article (2020)

Authors

Haluk Akay Massachusetts Institute of Technology
S.-G. Kim Massachusetts Institute of Technology
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Published Date

2020

Language

English

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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.