# Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies

arXiv: https://arxiv.org/abs/2311.11452 · HTML: https://mlsanity.com/paper/2311.11452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems](https://mlsanity.com/paper/2003.04919/) (2020)
- [Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks](https://mlsanity.com/paper/1906.05747/) (2019)
- [Adversarial Robustness vs Model Compression, or Both?](https://mlsanity.com/paper/1903.12561/) (2019)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Model Compression with Adversarial Robustness: A Unified Optimization Framework](https://mlsanity.com/paper/1902.03538/) (2019)
- [Compression of Deep Learning Models for Text: A Survey](https://mlsanity.com/paper/2008.05221/) (2020)
- [Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles](https://mlsanity.com/paper/2001.11086/) (2020)
- [Physics-Guided Deep Learning for Dynamical Systems: A Survey](https://mlsanity.com/paper/2107.01272/) (2021)
- [Knowledge Distillation in Deep Learning and its Applications](https://mlsanity.com/paper/2007.09029/) (2020)
- [Going Beyond Classification Accuracy Metrics in Model Compression](https://mlsanity.com/paper/2012.01604/) (2020)
- [Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems](https://mlsanity.com/paper/2105.00075/) (2021)
- [Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach](https://mlsanity.com/paper/2101.07128/) (2021)
- [Real-Time Machine Learning Enabled Low-Cost Magnetometer System](https://mlsanity.com/paper/2209.08065/) (2022)

## Abstract (arXiv)

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.