Machine learning is generally concerned with the development of computational systems that can improve their performance of a task with experience (typically in the form of data). Deep learning is a subset of machine learning that seeks to improve performance and robustness of computational systems by imbuing them with the ability to construct internal representations of the data at varying degrees of abstraction. Deep neural networks are an increasingly common method within these broader categories. Over the last decade, they have proven particularly useful for a variety of tasks including image recognition, sparking a variety of applications to related tasks in design. These have spanned architecture, mechanical design, product design, and game design. However, the relationship that these and other applications of deep learning have to the existing human stakeholders in design needs further exploration. In what way can deep learning augment or automate the behaviors of designers? In what way can it allow designers to better capture and respond to the needs, states, or behaviors of users and customers? How does deep learning connect to existing theoretical design frameworks?
The aim of this thematic collection is to bring together leading-edge research that focuses on the intersection of design and deep learning. Design Science is an archival publication venue that has a multidisciplinary readership. Since it is an online journal, papers in a Thematic Collection are published immediately upon acceptance. In keeping with open access principles, contributors are encouraged to make relevant software for their submission publicly accessible (e.g., as a GitHub repository or a Journal of Open-Source Software submission).
The deadline for full submissions is 31 May 2019.
Collection Theme Topics
Topics of interest include, but are not limited to, the following related to deep learning:
• Automating evaluation, analysis of potential solutions
• Enabling robust yet efficient simulations of complex systems
• Creating rich virtual worlds
• Automating the synthesis of novel solution concepts
• Facilitating intelligent interactive systems via machine learning constructs
• Engaging in human/machine co-creative design
• Leveraging deep learning for improved agent-based systems
• Generating unique design representations afforded by deep learning
• Christopher McComb, Penn State University (email@example.com)
• Kazjon Grace, University of Sydney (firstname.lastname@example.org)
• Akin Kazakci, MINES ParisTech (email@example.com)
• C. Tucker, “Latent Space Exploration of Deep Generative Design Models”
• G. Williams C. McComb, “From Form To Function and Back Again”
• Kazakci, M. Cherti, B. Kégl, “Digits that are not: Generating new types through deep neural nets”
• N. Manajan, M. Li, J. Menold, C. McComb, “Evaluating Human Trust in Deep Learning”
• S. Singaravel and P. Geyer, “Interpretation of hidden layer representation for design decisions”
• M. Fuge
• L. Kara