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 with adjoint sensitivity analysis capabilities. It is illustrated through an example.