CALL FOR PAPERS: Design by Data: Cultivating Datasets for Engineering 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 advanced deep learning and enabled tools like ChatGPT, Bard, and Stable Diffusion. Data-driven design is revolutionizing engineering design, enhancing design theory, […]
Constraining the Feasible Design Space in Bayesian Optimization With User Feedback
Authors: Cole Jetton, Matthew Campbell, Christopher Hoyle Abstract This paper develops a method to integrate user knowledge into the optimization process by simultaneously modelling feasible design space and optimizing an objective function. In engineering, feasible design space is a constraint similar to those in optimization problems. However, not all constraints can be explicitly written as […]
