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arXiv Machine Learning By Soroosh Noorzad, Sebastian Bodero, Morteza Fayazi

TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks

Read the original on arXiv Machine Learning →

TARGet is an open‑source, topology‑aware machine‑learning framework for modeling RF circuits. It uses S‑parameter representations of sub‑circuits and a fusion architecture that combines Graph Neural Networks with sub‑circuit connectivity‑aware networks, enabling learning across multiple topologies. Experiments show TARGet achieves sub‑1% prediction error, reduces training data needs by up to 35.5×, and improves accuracy by 9.7× over state‑of‑the‑art models, with zero‑shot transfer to unseen sub‑circuit topologies.

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