Physics-informed learning in artificial electromagnetic materials

The advent of artificial intelligence—deep neural networks (DNNs) in particular—has transformed traditional research methods across many disciplines. DNNs are data driven systems that use large quantities of data to learn patterns that are fundamental to a process. In the realm of artificial electromagnetic materials (AEMs), a common goal is to discover the connection between the AEM's geometry and material properties to predict the resulting scattered electromagnetic fields. To achieve this goal, DNNs usually utilize computational electromagnetic simulations to act as ground truth data for the training process, and numerous successful results have been shown. Although DNNs have many demonstrated successes, they are limited by their requirement for large quantities of data and their lack of interpretability. The latter results because DNNs are black-box models, and therefore, it is unknown how or why they work. A promising approach which may help to mitigate the aforementioned limitations is to use physics to guide the development and operation of DNNs. Indeed, this physics-informed learning (PHIL) approach has seen rapid development in the last few years with some success in addressing limitations of conventional DNNs. We overview the field of PHIL and discuss the benefits of incorporating knowledge into the deep learning process and introduce a taxonomy that enables us to categorize various types of approaches. We also summarize deep learning principles which are critical to PHIL understanding and the Appendix covers some of the physics of AEMs. A few specific PHIL works are highlighted and serve as examples of various approaches. Finally, we provide an outlook detailing where the field is currently and what we can expect in the future.