Interactive exploration of design space

Abstract

Typical design for manufacturing applications requires simultaneous optimization of conflicting performance objectives: Design variations that improve one performance metric may decrease another performance metric. In these scenarios, there is no unique optimal design but rather a set of designs that are optimal for different tradeoffs (called Pareto-optimal). In this thesis, I present a novel approach to discovering the Pareto front, allowing designers to navigate the landscape of compromises efficiently. The approach is based on a first-order approximation of the Pareto front, which allows entire neighborhoods rather than individual points on the Pareto front to be captured. In addition to allowing for efficient discovery of the Pareto front and the corresponding mapping to the design space, this approach allows one to represent the entire trade-off manifold as a small collection of patches that comprise a high-quality and piecewise-smooth approximation. Additionally, I will present the early stages of an extension to the aforementioned work - namely the capability to discover a Pareto gamut that arises from multiple fronts affected by one or more application variables.