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This article discusses the use of digital twinning and generative machine learning models for quantum force sensing in atomic Bose-Einstein condensates. By creating a digital twin that accurately represents the physical system, the authors were able to improve signal-to-noise ratio and sensitivity in their anomaly detection technique. This approach has potential applications in other sensing experiments involving high-dimensional data.