The scientific effort to control the interaction between light and matter has grown
exponentially in the last 2 decades. This growth has been aided by the development of …

Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good
representations by encouraging them to be invariant under meaningful transformations …

With the growing interest in the field of artificial materials, more advanced and sophisticated
functionalities are required from phononic crystals and acoustic metamaterials. This implies …

Abstract Structure scouting and design optimization for superior mechanical performance
through inverse machine learning is an emerging area of interest. Inverse machine learning …

In this work, a novel approach for the topology optimization of phononic crystals based on
the replacement of the computationally demanding traditional solvers for the calculation of …

In this paper, we review the specific field that combines topological photonics and deep
learning (DL). Recent progress of topological photonics has attracted enormous interest for …

The design of metamaterials which support unique optical responses is the basis for most
thin-film nanophotonic applications. In practice, this inverse design (ID) problem can be …

Designing physical artifacts that serve a purpose---such as tools and other functional
structures---is central to engineering as well as everyday human behavior. Though …

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the
complexity of the properties–structure–ingredients–process relationship of the different …