Magnetic Perturbation

Magnetic perturbation research focuses on understanding and predicting variations in Earth's magnetic field, primarily driven by solar wind interactions and internal plasma instabilities. Current research employs machine learning techniques, including neural networks (e.g., recurrent neural networks, Gaussian processes) and physics-informed neural networks, to improve forecasting accuracy and spatial resolution of geomagnetic disturbances, often using solar wind data as input. These advancements are crucial for mitigating the risks posed by geomagnetically induced currents to critical infrastructure and for enhancing our understanding of fundamental plasma physics processes in fusion energy research. Improved prediction models are leading to more accurate and timely warnings of geomagnetic storms and aiding in the development of effective plasma control strategies.