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Related Experiment Videos

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

N Shakespeare-Rees1, P W Livermore1, C J Davies1

  • 1School of Earth and Environment, University of Leeds, Woodhouse, Leeds, LS2 9JT UK.

Earth, Planets, and Space : EPS
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

The University of Leeds developed a new geomagnetic field model for the International Geomagnetic Reference Field (IGRF) forecast. This model, using Physics-Informed Neural Networks, shows improved accuracy for predicting secular variation (SV) from 2025-2030.

Keywords:
IGRF-14Outer Core FlowPhysics Informed Neural Networks (PINNs)Regional Inversion MethodsSecular Variation

Related Experiment Videos

Area of Science:

  • Geophysics
  • Earth Sciences
  • Computational Physics

Background:

  • The International Association of Geomagnetism and Aeronomy (IAGA) called for candidate models for the 14th generation of the International Geomagnetic Reference Field (IGRF).
  • Accurate forecasting of geomagnetic secular variation (SV) is crucial for various applications.

Purpose of the Study:

  • To present the University of Leeds' candidate model for the IGRF-14 forecast period (2025-2030).
  • To predict the average geomagnetic secular variation (SV) over a 5-year period.

Main Methods:

  • Inversion of the CHAOS-7.18 geomagnetic field model using Physics-Informed Neural Networks (PINNs).
  • Generation of two global mesh-free models: one from regional flows and one from a single global flow.
  • Advection of the magnetic field assuming steady core flow to construct the 5-year average SV forecast.

Main Results:

  • The model derived from regional flows demonstrated reduced Root Mean Square (RMS) misfit compared to CHAOS-7.18.
  • Hindcasts for the IGRF-13 period (2020-2025) showed improved performance against other candidate models.
  • The regional flow model provided a competitive candidate for the IGRF-14 forecast.

Conclusions:

  • The developed Physics-Informed Neural Network approach offers a robust method for geomagnetic field forecasting.
  • The regional flow model shows promise for accurate secular variation prediction.
  • Further refinements to the methodology could enhance future IGRF submissions.