Accelerating Space Mapping Optimization with Adjoint Sensitivities

We propose a procedure for accelerating the space mapping optimization process. Exploiting both fine- and surrogate-model sensitivity information, a good mapping between the two model spaces is efficiently obtained. This results in a significant speed-up over direct gradient-based optimization of the original fine model and enhanced performance compared with other space mapping approaches. Our approach utilizes commercially available software …