Special Issue on Design by Data: Cultivating Datasets for Engineering Design

Journal of Mechanical Design

In today's rapidly evolving world, data plays a pivotal role across various sectors including engineering. The success of data-driven methods in image and text analysis is largely due to adequately large datasets, which have enabled tools like ChatGPT and Stable Diffusion. Data-driven design is revolutionizing engineering design, enhancing design theory, decision-making, optimization, and educational curricula. However, challenges in adopting machine learning for data-driven design include limited high-quality publicly available datasets, datasets with insufficient number of samples and features, and a lack of real-world datasets. 

This special issue provides a platform for papers that investigate data-driven methods for design, provide insightful discussions about the data used, and publicly release their dataset. It also encourages submissions to address critical challenges in engineering dataset creation, such as multi-modality, handling missing information, managing diverse data types, navigating dependencies among attributes, ensuring data quality, and integrating complex data structures and real-time data. In particular, we seek papers that release datasets in, but not limited to, the following areas:

---

Topic Areas

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