Incorporating Value-Driven Design into the Visualization of Design Spaces Using Contextual Self-Organizing Maps: A Case Study of Satellite Design

摘要

This paper presents case study of satellite design using a new method to view n-dimensional design or optimization data using a contextual self-organizing map. The technique allows n-dimensional data to be trained without dimensionality reduction or other "compression". A designer can view similar design points in a true n-dimensional manner. Prior work in visual design space exploration using contextual self-organizing maps is extended through the incorporation of designer preferences using techniques founded in value-driven design. Standard techniques of the self-organizing map are used to group the n-dimensional design space into discrete clusters of similar characteristics through neural network-based topological ordering. Techniques from prior work are used to present the designer with a visual display encapsulating statistical qualities of value held within the n-dimensional design space. A case study involving a satellite design problem is presented and results compared with recent findings in value-driven design and traditional methods of design optimization. Initial results show unique potential of the combined methods to develop an understanding of the design space topology and to select promising areas of a design space for detailed analysis.