Design Optimization Utilizing Gradient/Hessian Enhanced Surrogate Model

By Wataru Yamazaki, M. Rumpfkeil, Dimitri J. Mavriplis. 2010

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Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “The authors of [210] obtain gradient and Hessian information with the adjoint method and automatic differentiation, and combine gradient and Hessian information into a co-kriging model.”
Review of multi-fidelity models (2016, methodology): “…to fuse multiple fidelity models, is regarded as a technique to construct an HFM approximation enhanced by data from LFMs. Relevant studies on co-Kriging methods can be found in Chung and Alonso (2002) [46], Forrester et al. (2008) [70], Yamazaki et al. (2010) [232] and Han et al. (2013) [88].”