mlsanityData to 7 Oct 2026

Data-Driven Design-by-Analogy: State of the Art and Future Directions

arXiv 2106.01592 ↗First posted 3 Jun 2021cs.AI cs.CEcited by 18

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Abstract (arXiv)

Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 18 of its 148 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

18 of 148 references.

References · 18

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6 more
  1. 2017Analogy Mining for Specific Design Needscited by 6
  2. 2020In Layman's Terms: Semi-Open Relation Extraction from Scientific Textscited by 6
  3. 2020Semantic Networks for Engineering Design: A Surveycited by 6
  4. 2020Design Knowledge Representation with Technology Semantic Networkcited by 5
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 2; 2024: 4; 2025: 3; 2026: 1.

18 papers.

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  1. 2022Generative Transformers for Design Concept Generationcited by 21
  2. 2021Engineering Knowledge Graph from Patent Databasecited by 18
  3. 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Reviewcited by 17
  4. 2022Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformerscited by 13
  5. 2025Inkspire: Supporting Design Exploration with Generative AI through Analogical Sketchingcited by 13
  6. 2021Deep Learning for Technical Document Classificationcited by 11
  7. 2024AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Modelscited by 8
  8. 2024Large Language Models for Combinatorial Optimization of Design Structure Matrixcited by 8
  9. 2021Toward AI Assistants That Let Designers Designcited by 6
  10. 2023Toward Artificial Empathy for Human-Centered Design: A Frameworkcited by 6
  11. 2021Patent Data for Engineering Design: A Critical Review and Future Directionscited by 5
  12. 2024Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systemscited by 3
6 more
  1. 2024Inspired by AI? A Novel Generative AI System To Assist Conceptual Automotive Designcited by 3
  2. 2021Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Datacited by 1
  3. 2022Generative Pre-Trained Transformers for Biologically Inspired Designcited by 1
  4. 2025Large Language Models for Combinatorial Optimization of Design Structure Matrixcited by 0
  5. 2025Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Eracited by 0
  6. 2026Beyond Input-Output: Rethinking Creativity through Design-by-Analogy in Human-AI Collaborationcited by 0