Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices

In this paper, we present a deep learning-based (DL-based) algorithm, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures. This approach is based on using the dimensionality reduction (DR) technique to significantly reduce the dimensionality of a generic EM wave-matter interaction problem without imposing significant error. Such an approach implicitly provides useful information about the role of different features (or design parameters such as geometry) of the nanostructure in its response functionality. To demonstrate the practical capabilities of this DL-based technique, we apply it to a reconfigurable optical metadevice enabling dual-band and triple-band optical absorption in the telecommunication window. Combination of the proposed approach with existing commercialized full-wave simulation tools offers a powerful toolkit to extract basic mechanisms of wave-matter interaction in complex EM devices and facilitate the design and optimization of nanostructures for a large range of applications including imaging, spectroscopy, and signal processing. It is worth to mention that the demonstrated approach is general and can be used in a large range of problems as long as enough training data can be provided

Design Optimization of Three-Layered Metamaterial Acoustic Absorbers Based on PVC Reused Membrane and Metal Washers

Waste management represents a critical issue that industrialized countries must necessarily deal with. Sustainable architecture involves the reuse of materials with the aim of significantly reducing the amount of waste produced. In this study, a new layered membrane metamaterial was developed based on three layers of a reused PVC membrane and reused metal washers attached. The membranes were fixed to a rigid support, leaving a cavity between the stacked layers. The samples were used to measure the sound absorption coefficient with an impedance tube. Different configurations were analyzed, changing the number of masses attached to each layer and the geometry of their position. These measurements were subsequently used to train a model based on artificial neural networks for the prediction of the sound absorption coefficient. This model was then used to identify the metamaterial configuration that returns the best absorption performance. The designed metamaterial behaves like an acoustic absorber even at low frequencies