Intelligent Gradient-Based Search of Incompletely Defined Design Spaces

Mark Schwabacher
Andrew Gelsey

Gradient-based numerical optimization of complex engineering designs
offers the promise of rapidly producing better designs.  However, such
methods generally assume that the objective function and constraint
functions are continuous, smooth, and defined everywhere.
Unfortunately, realistic simulators tend to violate these assumptions.
We present a knowledge-based technique for intelligently computing
gradients in the presence of such pathologies in the simulators, and
show how this gradient computation method can be used as part of a
gradient-based numerical optimization system.  We tested the resulting
system in the domain of conceptual design of supersonic transport
aircraft, and found that using knowledge-based gradients can decrease the
cost of design space search by one or more orders of magnitude.