The data-science revolution is poised to transform the way photonic systems are simulated
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …

Nanostructured surfaces with designed optical functionalities, such as metasurfaces, allow
efficient harvesting of light at the nanoscale, enhancing light–matter interactions for a wide …

Conventional human-driven methods face limitations in designing complex functional
metasurfaces. Inverse design is poised to empower metasurface research by embracing fast …

In the recent years, a dramatic boost of the research is observed at the junction of photonics,
machine learning and artificial intelligence. A new methodology can be applied to the …

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 …

Abstract Machine learning, as a study of algorithms that automate prediction and decision‐
making based on complex data, has become one of the most effective tools in the study of …

Metasurfaces have shown promising potentials in shaping optical wavefronts while
remaining compact compared to bulky geometric optics devices. The design of meta-atoms …

Remarkable advancements have been made in the design of optical metasurfaces in recent
years, particularly in compact designs. However, for their practical integration into diverse …

In this paper, we propose a deep learning approach for forward modeling and inverse
design of photonic devices containing embedded active metasurface structures. In …