3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks
Accurate reconstruction of magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such as spherical harmonic expansion, often suffer from truncation errors that limit their precision. This study proposes an advanced Physics-Informed Neural Network (PINN) framework for high-precision 3D magnetic field mapping. Unlike conventional data-driven models, the proposed PINN integrates Maxwell's equations directly into the loss function, enforcing divergence-free and curl-free conditions across the entire domain. A key innovation is the inclusion of explicit physics-residual losses at measurement locations, ensuring rigorous physical c...
| Authors | Haohan Yu, Zhanxu Hao, Bingzhi Li et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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