Design Representation Learning

Design Representation Learning is a computational framework in Evolutionary Product Design that systematically acquires and encodes latent design knowledge from product data through algorithmic models, distinguishing itself by enabling automated, data-driven generalization of design principles for generative innovation.

154

Publications

6.8K

Citations

430

Authors

173

Institutions

Publications per year

2017–2026

99

Authors

430

  1. AB
    Alex Burnap

    University of Michigan, United States

    5 publications · 173 citations

    5

    h-index

  2. CM
    Christopher McComb

    Carnegie Mellon University, United States

    5 publications · 167 citations

    5

    h-index

  3. JC
    Jonathan Cagan

    Carnegie Mellon University, United States

    4 publications · 144 citations

    4

    h-index

  4. PY
    Panos Y. Papalambros

    University of Michigan, United States

    4 publications · 149 citations

    4

    h-index

  5. AR
    Ayush Raina

    Carnegie Mellon University, United States

    4 publications · 144 citations

    4

    h-index

1–5 of 430

Venues

  1. Journal of Mechanical Design

    13 publications · 613 citations

    13

    h-index

  2. Advanced Engineering Informatics

    7 publications · 211 citations

    7

    h-index

  3. Computer-Aided Civil and Infrastructure Engineering

    CACAIE · 4 publications · 290 citations

    4

    h-index

  4. Artificial Intelligence in Engineering

    4 publications · 174 citations

    4

    h-index

  5. International Journal of Architectural Computing

    IJAC · 3 publications · 135 citations

    3

    h-index

1–5