Multi Level Modeling for Engineering Design Optimization

Physical systems can be modeled at many levels of approximation The right model de pends on the problem to be solved In many cases a combination of models will be more e ective than a single model alone Our research investigates this idea in the context of en gineering design optimization We present a family of strategies that use multiple models for unconstrained optimization of engineering designs The strategies are useful when multiple approximations of an objective function can be implemented by compositional modeling tech niques 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 su cient conditions for correctness and convergence We also report experimental tests of our methods in the domain of sailing yacht design Our results demonstrate dramatic reductions in the CPU time required for optimization on the problems we tested with no signi cant loss in design quality Arti cial Intelligence for Engineering Design Analysis and Manufacturing