Advancements in Physics-Informed Neural Networks for Solving Maxwell’s Equations: A Systematic Literature Review

This systematic literature review (SLR) investigates the use of physics-informed neural networks (PINNs) in electromagnetics by examining peer-reviewed journal articles and conference papers. By integrating governing physical laws into the loss function of a neural network (NN), PINNs offer a promising mesh-free method in scientific computing. The reviewed records were retrieved from the databases Scopus, Web of Science, and IEEE Xplore, published between 2020 and 2025. The initial dataset comprised 500 records of which 292 unique publications were identified. These were screened, yielding a final set of 139 publications that met predefined inclusion criteria. The analysis reveals a growth in research activity, with a pronounced increase from 2022 onward. The reviewed literature predominantly addresses electrodynamic problems, employs feedforward neural network architectures and adopts unsupervised, physics-driven training paradigms. Two-dimensional problem formulations are considerably more prevalent than three-dimensional formulations, and advanced architectures remain limited. A contingency table analysis reveals the associations between the extracted characteristics. The choice of medium is strongly dependent on the physics regime, and architectural diversity increases with spatial dimensionality. The review’s conclusions identify potential priorities for future work: extending three-dimensional formulations to the static and quasistatic electromagnetic regimes, broader architectural experimentation particularly in lower-dimensional settings, and increased use of semi-supervised learning in static electromagnetic applications.