Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …

Visualizing and analyzing multiple Pareto-optimal solutions obtained using an evolutionary
multi-or many-objective optimization algorithm is as important a task as the task of finding …

Graphical optimization allows solving one or two dimensional optimization problems visually
by merely plotting the objective function and constraint function contours. In addition to the …

In multi-criterion optimization, decision-makers (DMs) are not often interested in the
complete Pareto-optimal front. Instead, they have preferences favoring specific parts of the …

In the design evolution of a product, designers often require solving similar functions
repeatedly across different designs. These functions are usually related to each other and …

Evolutionary algorithms (EAs) are widely employed in multi-objective optimization (MOO) to
find a well-distributed set of near-Pareto solutions. Among various types of practicalities that …

Engaging with performance feedback in early building design often involves building a
custom parametric model and generating large datasets, which is not always feasible …

Visualization techniques in design space exploration with high dimensional data are helpful
in enhancing the decision making in the context of multiple objective optimization …

Design automation is undergoing a new generation of changes caused by artificial
intelligence technologies represented by deep learning and reinforcement learning …

Abstract Design preferences or targets are typically available at system level. A designer is
usually interested in understanding patches of design space at component levels, across …