Enhancing Magnetization Mapping with Neural Networks

Reconstructing magnetization maps from measured magnetic stray-field images is a challenging inverse problem, often limited by numerical artifacts and ill-posed transformations. To address this, researchers around Patrick Maletinsky developed a neural-network-based approach that incorporates physically informed loss functions to improve reconstruction accuracy.

Their method outperforms traditional techniques, effectively reducing artifacts and ensuring robust performance across different magnetization directions and measurement-axis orientations. While demonstrated using nitrogen-vacancy (NV) center magnetometry, this approach is measurement-agnostic, making it applicable to a broad range of inverse problems in physics and engineering.