Multifidelity Surrogate Modeling Based on Analytical Eigenfunction Expansions

In this work, a new method is proposed to derive the initial approximate model for a multifidelity (MF) surrogate optimization. Specifically, the proposed method is trained using a set of eigenfunction expansions that characterize the solution domain of the desired geometry and high-fidelity (HF) full-wave simulations. To demonstrate and validate the proposed method, an array of loops, a pyramidal horn antenna, and patch antennas of arbitrary shapes are studied. Notably, the proposed MF method is applied and tested in single- and multiobjective optimization settings to achieve two or three design goals. Our studies illustrate that the proposed eigenfunction expansion-based method can create approximate models needed in MF optimizations up to 243 times faster than the conventional coarse mesh low-fidelity (LF) approaches. This in turn makes the total training time of our MF models up to 2.8 times shorter than the conventional MF models.