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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 trade-offs (called Pareto-optimal). In this
work, we propose a novel approach to discover the Pareto front, allowing designers
to navigate the landscape of compromises efficiently. Our 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 us to represent the entire trade-off manifold as a small collection of
patches that comprise a high-quality and piecewise-smooth approximation.
We illustrate how this technique can be used for navigating performance
trade-offs in computer-aided design (CAD) models.
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