面向NV金刚石磁强计仿真到现实校准的物理引导机器学习
Physics-guided machine learning for sim-to-real calibration of NV diamond magnetometers
浏览论文内容
中文总结 AI 辅助
本研究提出嵌入塞曼分裂的物理引导混合机器学习框架，解决NV金刚石磁强计仿真到现实匹配偏差问题，实现372倍精度提升，为自校准传感器及数据稀缺物理系统的ML训练提供方法。
中文摘要 AI 辅助
金刚石中的集成氮空位（NV）中心可在无屏蔽环境中实现鲁棒的矢量磁测量，但其部署仍受限于复杂的校准流程及对外部数据参考的依赖。传统统计机器学习需要极其庞大的训练数据，且存在严重的仿真到现实的匹配偏差问题。为解决该问题，我们提出一种物理引导的混合机器学习框架，该框架将塞曼分裂直接嵌入学习流程。我们的物理引导模型显著降低了平均跟踪误差，相较于纯统计基线实现了372倍的精度提升。此外，我们的混合架构将稀疏物理测量与可扩展合成数据生成相结合，无缝融入真实硬件的非理想特性。当用于解码未校准的原始实验ODMR数据时，该框架对标量磁场展现出优异的预测精度。本研究为自校准传感器铺平了道路，同时建立了一种适用于其他数据稀缺物理系统的机器学习训练方法。
英文摘要
Ensemble nitrogen-vacancy (NV) centers in diamond enable robust vector magnetometry in unshielded environments, yet deployment remains bottlenecked by complex calibration and a reliance on external data references. Conventional statistical machine learning requires an exorbitantly large volume of training data and suffers from severe simulation-to-reality mismatches. To address this, we introduce a physics-guided hybrid machine learning framework that embeds the Zeeman splitting directly into the learning pipeline. Our physics-guided model significantly reduces the average tracking error demonstrating a 372-fold precision improvement over purely statistical baselines. Furthermore, our hybrid architecture pairs a sparse physical measurement with scalable synthetic data generation, seamlessly incorporating real-world hardware non-idealities. When deployed to decode uncalibrated, raw experimental ODMR data, our framework delivers exceptional predictive accuracy for the scalar magnetic field. This work paves the way toward self-calibrated sensors while establishing a machine learning training method applicable to other data-scarce physical systems
