Metamaterials provide the opportunity for designers to create customisable artificial materials by independently tailoring the electric and magnetic response of sub-wavelength geometric structures to electromagnetic energy. Due to the increased complexity of these geometric structures, exacerbated by the increased interest in generating inhomogeneous and anisotropic metamaterials, direct optimisation of these designs using conventional approaches often becomes impractical and limited. In order to alleviate this issue, we propose an alternative optimisation approach which exploits the Kriging methodology in conjunction with an adaptive sampling plan to simultaneously optimise multiple conflicting objectives. Results show the effectiveness of the outlined algorithm in calculating a uniform spread of optimal trade-off designs, balancing the real and imaginary components of the refractive index over a wide range of values.
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