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ACM Netw."],"published-print":{"date-parts":[[2025,11,24]]},"abstract":"<jats:p>Machine learning (ML) models have emerged as the state-of-the-art approach for wireless applications such as transmitter localization. One of the challenges for ML models, however, is their reliance on the abundance of high quality data. Specifically for localization, a significant challenge is to obtain training data that ''covers'' the entire landscape, in order to ensure a high accuracy for the ML approaches. This issue is compounded when trying to localize multiple transmitters. To address this problem, we introduce a new data augmentation pipeline, termed Physics-informed Augmentation and RF Modeling (PhARMNet), that can combine existing data with a physics-based simulation model, producing a larger dataset with an improved coverage of the landscape of interest. Our results show that PhARMNet offers significant advantages over traditional path loss models. 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