Multi-Fidelity Surrogate Modeling based on Numerical Eigenfunction Expansions Conference

Sendrea, RE, Zekios, CL, Georgakopoulos, SV. (2023). Multi-Fidelity Surrogate Modeling based on Numerical Eigenfunction Expansions . 10.23919/ACES57841.2023.10114761

cited authors

  • Sendrea, RE; Zekios, CL; Georgakopoulos, SV

abstract

  • In this work, a novel physics-based method for supplying low-fidelity models is studied. Specifically, numerical eigenfunction expansions are used to efficiently provide approximate electromagnetic solution data for a multi-fidelity (MF) surrogate optimization process. Based on our results, the proposed method can generate highly accurate MF models 3 times faster compared to state-of-the-art methods and with errors less than 3% when compared with full-wave solutions.

publication date

  • January 1, 2023

Digital Object Identifier (DOI)

International Standard Book Number (ISBN) 13