We develop fast, accurate and versatile optimization algorithms and we derive physics-informed machine learning models that can study, analyze and design antennas and microwave devices.
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.
R. E. Sendrea, C. L. Zekios and S. V. Georgakopoulos, “Multifidelity Surrogate Modeling Based on Analytical Eigenfunction Expansions,” in IEEE Transactions on Antennas and Propagation, vol. 71, no. 2, pp. 1673-1683, Feb. 2023, doi: 10.1109/TAP.2022.3228615.