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    The American Society of Mechanical Engineers

    Special Issue on Generative Artificial Intelligence in Design

    Journal of Mechanical Design
    Journal of Mechanical Design icon
    Engineering design is undergoing a profound transformation with the rise of generative artificial intelligence (AI). Generative AI focuses on creating new samples resembling its training data, using methods such as variational autoencoders, generative adversarial networks, diffusion models, and large language models. Generative Artificial Intelligence in Design (GAIDe) unlocks the ability to produce novel design alternatives across high-dimensional, multimodal spaces, accelerating innovation and expanding the scope of exploration for diverse objectives. GAIDe is enabling new ways of ideating, exploring, and optimizing designs across a wide array of domains. From conceptual ideation to manufacturability, from materials to sustainable systems, GAIDe represents a new frontier for how we approach complexity, creativity, and trade-off management in engineering design. At the same time, GAIDe introduces unique challenges compared to other fields, for example, limited and noisy design and engineering data, the need to respect physical or mechanical constraints, and domain-specific design principles and considerations. 

    Topic Areas

    THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

    • Generative design methods for conceptual design and early-stage ideation
    • Social and ethical implications of generative AI use in engineering design 
    • Applications of generative AI in systems design
    • Integration of generative AI with materials design
    • Human-AI co-design, creativity support, and interaction frameworks
    • Design optimization by integrating generative AI
    • Physics-informed or constraint-aware generative AI in engineering design
    • Benchmarking GAIDe against conventional or hybrid engineering design methods
    • Novel datasets designed to support applications of AI in design 
    • Use of multi-modal datasets (text, image, simulation, user data) in the design process

    Special Issue Publication Dates


    Paper submission deadline: February 15, 2026
    Initial review completed: April 1, 2026
    Publication date: October 2026

    Submission Instructions

    Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here. 

    Once at the Paper Submittal page, select the Journal of Mechanical Design, and then select the Special Issue on Generative Artificial Intelligence in Design.

    Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.

    Guest Editors

    Xingang Li, University of Melbourne, Australia (xingang.li@unimelb.edu.au)

    Madhurima Das, University of Melbourne, Australia (madhurrima.das@unimelb.edu.au)

    Katja Holtta-Otto, University of Melbourne, Australia (katja.holttaotto@unimelb.edu.au)

    Zhenghui Sha, University of Texas at Austin, USA (zsha@austin.utexas.edu)

    Christopher McComb, Carnegie Mellon University, USA (ccm@cmu.edu)

    Liwei Wang, Carnegie Mellon University, USA (liweiw@andrew.cmu.edu)

    Ferdous Alam, Massachusetts Institute of Technology, USA (mfalam@mit.edu)