Physics-informed data-driven modelling of Earth’s magnetic environment
- Academic lead
- Phil Livermore, School of Earth and Environment, [email protected]
- Co-supervisor(s)
- Amir Khan, School of Civil Engineering, [email protected], Trystan Surawy-Stepney, School of Earth and Environment, [email protected], Chetan Deva, School of Earth and Environment, [email protected]
- Project themes
- Astrophysics & Geophysics, Computational & Analytical Tools, Data-driven methods, Multiphysics & Complex Fluids
Fluid dynamics play a crucial role in controlling Earth’s magnetic environment. Motion of electrically-conducting fluid deep within Earth’s core creates our planet’s internally-generated field, while the charged solar wind emitted from the sun creates an external magnetic field through planetary-scale high-altitude electrical currents which drive phenomena like aurorae and space-weather. Understanding our magnetic environment is not only fundamental science, but of major importance to how we mitigate severe space-weather hazard for ground-based infrastructure such as powergrids and the 10,000 currently orbiting satellites.
Data from the current satellite missions Swarm and MSS-1, as well as imminent launches of MSS-2 and Nanomagsat, could begin a new era of data-led modelling of our magnetic environment. These spacecraft fly through near-Earth space, recording the local electromagnetic environment, and provide an unprecedented dataset of how the magnetic field varies both in time but also by location.
Based on these data, and state-of-the-art methods in machine learning, the student will create new reconstructions of our magnetic field environment. The project requires a general interest in Earth’s magnetic field and an ambition to learn and implement data-driven modelling approaches.
