Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design

TL;DR

This paper reviews how physics-informed neural networks and AI techniques are revolutionizing electromagnetic and nanophotonic device design, enabling faster, more efficient, and more accurate optimization and simulation methods.

Contribution

It provides a comprehensive survey of recent AI-driven methods in electromagnetic and nanophotonics, highlighting innovations in forward/inverse design, simulation acceleration, and physical law integration.

Findings

AI accelerates electromagnetic simulations and modeling.

Physics-informed neural networks improve design efficiency.

Enhanced capabilities in nonlinear and topological photonics.

Abstract

The fusion of artificial intelligence (AI) with physics-guided frameworks has opened transformative avenues for advancing the design and optimization of electromagnetic and nanophotonic systems. Innovations in deep neural networks (DNNs) and physics-informed neural networks (PINNs) now provide robust tools to tackle longstanding challenges in light scattering engineering, meta-optics, and nonlinear photonics. This review outlines recent progress in leveraging these computational methodologies to enhance device performance across domains such as dynamic light modulation, antenna design, and nonlinear optical phenomena. We systematically survey advancements in AI-driven forward and inverse design strategies, which bypass conventional trial-and-error approaches by embedding physical laws directly into optimization workflows. Furthermore, the integration of AI accelerates electromagnetic simulations and enables precise modelling of complex optical effects, including topological photonic states and nonlinear interactions. A comparative evaluation of algorithmic frameworks highlights their strengths in balancing computational efficiency, multi-objective optimization, and fabrication feasibility. Challenges such as limited interpretability of AI models and data scarcity for unconventional optical modes are critically addressed. Finally, we emphasize future opportunities in scalable multi-physics modelling, adaptive architectures, and practical deployment of AI-optimized photonic devices. This work underscores the pivotal role of AI in transcending traditional design limitations, thereby propelling the development of next-generation photonic technologies with unprecedented functionality and efficiency.