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Applying nonlinear optimization strategies directly to complex multidisciplinary systems can be prohibitive when the complexity of the simulation codes is large. In- creasingly, response surface approximations(RSAs), and specifically quadratic approximations, are being inte- grated with nonlinear optimizers in order to reduce the CPU time required for the optimization of complex mul- tidisciplinary systems. RSAs provide a computationally inexpensive lower fidelity representation of the system performance. The curse of dimensionality is a major drawback in the implementation of these approximations as the amount of required data grows quadratically with the number of design variables. In this paper a novel technique to reduce the magni- tude of the sampling to O(n) is presented. The technique uses prior information to approximate the eigenvectors of the Hessian matrix and only requires the eigenvalues

Reduced sampling for construction of quadratic response surface approximations using adaptive experimental design