  Space weather phenomena like geomagnetic disturbances (GMDs) and
geomagnetically induced currents (GICs) pose significant risks to critical
technological infrastructure. While traditional predictive models, grounded in
simulation, hold theoretical robustness, they grapple with challenges, notably
the assimilation of imprecise data and extensive computational complexities. In
recent years, Tiny Machine Learning (TinyML) has been adopted to develop
Machine Learning (ML)-enabled magnetometer systems for predicting real-time
terrestrial magnetic perturbations as a proxy measure for GIC. While TinyML
offers efficient, real-time data processing, its intrinsic limitations prevent
the utilization of robust methods with high computational needs. This paper
developed a physics-guided TinyML framework to address the above challenges.
This framework integrates physics-based regularization at the stages of model
training and compression, thereby augmenting the reliability of predictions.
The developed pruning scheme within the framework harnesses the inherent
physical characteristics of the domain, striking a balance between model size
and robustness. The study presents empirical results, drawing a comprehensive
comparison between the accuracy and reliability of the developed framework and
its traditional counterpart. Such a comparative analysis underscores the
prospective applicability of the developed framework in conceptualizing robust,
ML-enabled magnetometer systems for real-time space weather forecasting.
