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arXiv · 2610.05004

Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design

Avi Epstein·Snir Nehemia·Haim Suchowski·Lior Wolf
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Abstract

Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction and inverse design. We present a mesh-native, physics-augmented graph-learning framework that treats radiation-pattern prediction as signal reconstruction on an irregular surface mesh. For the forward problem, a GPS graph transformer is trained with Physics-Augmented Intermediate Supervision (PAIS), an auxiliary node-level objective that predicts complex surface currents, the physical intermediate linking geometry to radiation. PAIS improves multiple GNN backbones at no inference-time cost, while shuffled-current and non-physical controls show the gain comes from physical correspondence. Direction-conditioned decoding and a differentiable radiation-integral consistency loss further exploit this structure. On an 80,000-sample CST benchmark, GPS+PAIS reaches MSE 0.17 / PSNR 19.67, generalizes to a PCA split, and transfers zero-shot to canonical patches. For inverse design, surrogate-filtered diffusion beats nearest-neighbor retrieval by 32% relative MSE.

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Avi Epstein, Snir Nehemia, Haim Suchowski, Lior Wolf. 2026-10-04. Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design. https://arxiv.org/abs/2610.05004

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