Volume 53, Number 2, 2022Machine learning in physics
|Page(s)||18 - 21|
|Published online||27 April 2022|
Deep learning for magnetism
Department of Energy Conversion and Storage, Technical University of Denmark, 2800 Kgs, Lyngby, Denmark
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.
© European Physical Society, EDP Sciences, 2022
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