Surrogate Modeling with Low-Rank Function Representation for Electromagnetic Simulation

TL;DR

This paper introduces a low-rank tensor function representation framework for electromagnetic surrogate modeling, improving accuracy and efficiency in high-dimensional design spaces compared to existing neural methods.

Contribution

It proposes a novel pairwise low-rank tensor network (PLRNet) for EM surrogate modeling, systematically studying low-rank formats and demonstrating superior performance.

Findings

Achieves better accuracy and robustness in EM surrogate tasks.

Offers parameter-efficient models with stable high-dimensional optimization.

Outperforms existing neural surrogate methods in experiments.

Abstract

High-fidelity electromagnetic (EM) simulations are indispensable for the design of microwave and wave devices, yet repeated full-wave evaluations over high-dimensional design spaces are often computationally prohibitive. While neural surrogates can amortize this cost, learning high-dimensional EM response mappings remains difficult under limited simulation budgets due to strong and heterogeneous parameter couplings. In this work, we introduce low-rank tensor function representations as a principled surrogate modeling paradigm for EM problems and provide a systematic study of representative low-rank formats, including Tucker-style low-rank tensor function representation (LRTFR) as well as neural functional tensor-train (TT) and tensor-ring (TR) baselines. Building on these insights, we propose a pairwise low-rank tensor network (PLRNet) that uses learnable pairwise interaction factors over compact coordinate-wise embeddings. Experiments on representative EM surrogate tasks demonstrate that the proposed framework achieves a more favorable overall trade-off between accuracy, robustness, and parameter efficiency, with stable optimization in high-dimensional regimes.