Abstract
Physical systems can be modeled at many levels of approximation. The right model depends on the problem or subproblem to be solved. In many cases, a combination of models will be more effective than a single model alone. Our research investigates this idea in the context of engineering design optimization. We present a family of strategies for using multiple models to optimize engineering designs. The strategies are useful when multiple approximations of an objective function can be implemented by compositional modeling techniques. We show how a compositional modeling library can be used to construct a variety of locally calibratable approximation schemes that can be incorporated into the optimization strategies. We analyze the optimization strategies and approximation schemes to formulate and prove sufficient conditions for correctness and convergence. We also report experimental tests of our methods in the domain of sailing yacht design. Our results demonstrate a dramatic reduction in the CPU time required for optimization, with no significant loss in design quality.