![]() Later, in the early 2000s, neural networks were used to design photonic crystal fibers. The design of photonic devices by machine learning started back in the 1990s with the optimization of microwave circuit components, starting with recurrent neural networks and then transitioning to feed-forward neural networks. Networks with thousands of trainable parameters can be systematically optimized using algorithms such as stochastic steepest descent and error backpropagation in a reasonable amount of time using commonly available computing resources. Once the architecture is defined, the network is typically trained by employing a set of input data with corresponding desired outputs. The network must be sufficiently complex to encode the problem at hand but not so complex as to resist training. The choice of the structure of the network, i.e., its architecture, is still mostly a matter of taste and experience rather than a result of clearly established principles. Briefly, a deep-learning model is an artificial neural network that converts vectors of input data into vectors of output data through a series of transformations characterized by a large number of trainable parameters. Recently, there has been a surge of interest in employing machine learning, especially deep learning, to tackle the limitations of traditional approaches. However, this approach is often time-consuming, applicable only to relatively simple geometries, and very sensitive to measurement noise. Traditionally, these tasks have been based on algorithmically solving Maxwell’s equations for a given setup geometry, whose parameters need to be determined from experimental measurements. To optimize the performance of such applications, the ability to predict and analyze light–matter interactions is crucial. ![]() The interaction of light with matter at the subwavelength scale constitutes the foundation for many applications, ranging from microscopy and nanosensors to data storage and optical communications.
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