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<p>Traditional geomagnetic field modeling requires balancing computational efficiency with the resolution of complex dynamics. A dual-architecture deep learning framework is introduced as a high-efficiency surrogate for global modeling. A sinusoidal representation network (SIREN) captures the continuous, quasi-static internal field, and a transformer with an ‘adaptive recursion’ mechanism models the dynamic external field. The recursive mechanism adjusts computational depth according to geomagnetic activity, optimizing efficiency and accuracy. Trained on CHAOS-8.4 data, the framework reproduces the global field with high fidelity. Under identical CPU hardware conditions, the SIREN-based surrogate model achieved an inference speed-up of approximately 1100 times compared to traditional implementations. Native graphics processing unit (GPU) acceleration further enables large scale parallel computations without additional engineering effort. This study validated the feasibility of deep learning as a robust real-time surrogate for complex geophysical simulations.</p>

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Geomagnetic field modeling with dual-architecture neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

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  • Zhijin Bao,
  • Junhui Xing,
  • Haowei Xu,
  • Jiayi Wei,
  • Chong Xu

摘要

Traditional geomagnetic field modeling requires balancing computational efficiency with the resolution of complex dynamics. A dual-architecture deep learning framework is introduced as a high-efficiency surrogate for global modeling. A sinusoidal representation network (SIREN) captures the continuous, quasi-static internal field, and a transformer with an ‘adaptive recursion’ mechanism models the dynamic external field. The recursive mechanism adjusts computational depth according to geomagnetic activity, optimizing efficiency and accuracy. Trained on CHAOS-8.4 data, the framework reproduces the global field with high fidelity. Under identical CPU hardware conditions, the SIREN-based surrogate model achieved an inference speed-up of approximately 1100 times compared to traditional implementations. Native graphics processing unit (GPU) acceleration further enables large scale parallel computations without additional engineering effort. This study validated the feasibility of deep learning as a robust real-time surrogate for complex geophysical simulations.