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Multifidelity Modeling Based on Numerical Eigenfunction Expansions
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AI
DOI：10.1109/TAP.2026.3651512.png)
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Abstract
En 中文
In this work, a novel method for constructing low-fidelity (LF) models tailored for eigenanalysis-based multifidelity (MF) surrogate optimization frameworks is proposed. Namely, the proposed approach leverages reference eigenfunction expansions (EEs) to solve a properly formulated boundary value problem (BVP), enabling the rapid computation of characteristic mode solutions for arbitrary structures. To ensure the uniqueness and well-posedness of the BVP, a computationally efficient 2-D method of moments (MoMs) solution is employed to define the necessary boundary conditions. The resulting LF solutions are incorporated into an MF surrogate modeling scheme by combining them with data from high-fidelity (HF) simulations. To enable consistent training and improve convergence, the characteristic mode solutions are organized through a robust correlation-based matching scheme. The proposed method is validated through two multiobjective characteristic mode analyses of microstrip patch antennas. Notably, our results demonstrate that the eigenfunction-based LF model yields solutions over three orders of magnitude faster than state-of-the-art coarse-mesh-based LF approaches. This acceleration translates to an approximate twofold reduction in total training time for the MF surrogate model, compared to conventional MF strategies.
Keywords:
Machine learning
design optimization
characteristic mode analysis
method-of-moments
multi-fidelity methods
analytical models
Journal
IF:
5.8
Papers:
502
Citations:
6.8W