Forecasting secular variation using physics-informed neural networks for IGRF-14

In response to the call for candidate models for the 14th generation of the International Geomagnetic Reference Field (IGRF) by the Geomagnetic Field Modeling Working Group (V-MOD) of the International Association of Geomagnetism and Aeronomy (IAGA), we present the University of Leeds candidate model for the forecast of the average Secular Variation (SV) for 2025–2030. Our approach consists of inverting the geomagnetic field model CHAOS−7.18 using Physics-Informed Neural Networks to produce two global mesh-free models from (i) a mosaic of independent regional flows and (ii) a single global flow representation. The magnetic field is then advected under the assumption of steady core flow over a 5-year period, and the average SV over 5 years is taken to construct the forecast. We validate our approach using hindcasts for the IGRF-13 time period (2020–2025) and benchmark our methodology against the inferred SV from CHAOS−7.18. Our field models constructed from regional flows show reduced RMS misfit relative to the field from the CHAOS−7.18 model at each yearly timestep, compared to the other candidate models from IGRF-13 in the hindcast, both at the Core Mantle Boundary and at the Earth’s Surface. We then present our IGRF-14 candidate forecast for the period 2025–2030, derived from the regional method, and discuss possible improvements to this method for future IGRF submissions.