Deep learning for magnetism

In deep learning, neural networks consisting of trainable parameters are designed to model unknown functions based on available data. When the underlying physics of the system at hand are known, e.g., Maxwell’s equation in electromagnetism, then these can be embedded into the deep learning architecture to obtain better function approximations.