Electromagnetic Imaging

Electromagnetic imaging aims to reconstruct internal structures from external electromagnetic measurements, a challenging inverse problem often hampered by ill-posedness and computational cost. Current research heavily utilizes machine learning, particularly deep learning architectures like generative adversarial networks (GANs) and neural networks integrated with iterative methods such as Born iterations, to improve accuracy and efficiency. These approaches often incorporate physical constraints to enhance robustness and generalizability, addressing limitations of purely data-driven methods. The resulting advancements hold significant promise for diverse applications, including medical imaging (e.g., brain source localization), geophysical exploration, and non-destructive testing.