Very innovative application of NN architecture in a different (physics/optics) domain !<p>> The key idea is that there is a strong analogy to be made between layers of a neural network, and optical elements in a so-called sequential optical system. If we have a compound optical system made of a series of lenses, mirrors, etc., we can treat each optical element as the layer of a neural network. The data flowing through this network are not images, sounds, or text, but rays of light. Each layer affects light rays depending on its internal parameters (surface shape, refractive material...) and following the very much non‑linear Snell's law. Inference, or the forward model, is the optical simulation where given some input light, we compute the system's output light. Training, or optimization, is finding the best shapes for lenses to focus light where we want it.