Predicting physical response of an artificially structured material is of particular interest for
scientific and engineering applications. Here we use deep learning to predict optical …

While deep learning has demonstrated tremendous potential for photonic device design, it
often demands a large amount of labeled data to train these deep neural network models …

Data inconsistency leads to a slow training process when deep neural networks are used for
the inverse design of photonic devices, an issue that arises from the fundamental property of …

A novel conditional variational autoencoder (CVAE) model for designing nanopatterned
integrated photonic components is proposed. In particular, it is shown that prediction …

A central challenge in the development of nanophotonic structures is identifying the optimal
design for a target functionality, and understanding the physical mechanisms that enable the …

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as
light concentration, routing, and filtering. Designing these devices to achieve precise light …

A major difficulty in applying computational design methods to nanophotonic devices is
ensuring that the resulting designs are fabricable. Here, we describe a general inverse …

Machine learning (ML) techniques, such as neural networks, have emerged as powerful
tools for the inverse design of nanophotonic structures. However, this innovative approach …

Photonic inverse design concerns the problem of finding photonic structures with target
optical properties. However, traditional methods based on optimization algorithms are time …

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 …