Abstract
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the ob jective function and constraint functions are continuous, smooth, and de- ned 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.