arXiv:2605.25640v1 Announce Type: cross Abstract: 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 acr
The increasing complexity and data volume in high-precision physics experiments are pushing the limits of traditional magnetic field mapping methods, making advanced AI solutions increasingly necessary.
This development enhances the accuracy and efficiency of fundamental physics research and could translate into industrial applications requiring precise field control or measurement.
The ability to reconstruct magnetic fields in inaccessible regions with higher precision and fewer errors using physics-informed AI models is significantly improved.
- · High-precision physics experiments
- · Particle accelerators
- · Fusion research
- · AI/ML in scientific computing
- · Traditional magnetic field mapping techniques
- · Experiments limited by measurement precision
More accurate experimental results in fields relying on magnetic field control and measurement.
Accelerated discovery timelines in areas like materials science, medical imaging, and energy fusion due to enhanced diagnostic capabilities.
New engineering capabilities for designing devices that operate within highly constrained or complex magnetic environments.
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Read at arXiv cs.LG