Metamaterials enable tailoring of light–matter interactions, driving discoveries which fuel novel applications. Deep neural networks (DNNs) have shown marked achievements in metamaterials research, however they are black boxes, and it is unknown how they work. We present a causal DNN where the learned physics is available to the user. Here, the condition of causality is enforced through a deep Lorentz layer which takes in the geometry of an all-dielectric metamaterial, and outputs the causal frequency-dependent permittivity and permeability. The ability of the LNN to learn metamaterial physics is verified with examples, and results are compared to theory and simulations.
Deep neural networks are empirically derived systems that have transformed research methods and are driving scientific discovery. Artificial electromagnetic materials, such as electromagnetic metamaterials, photonic crystals, and plasmonics, are research fields where deep neural network results evince the data driven approach; especially in cases where conventional computational and optimization methods have failed. We propose and demonstrate a deep learning method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated.
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