This systematic literature review investigates the use of physics-informed neural networks (PINNs) in electromagnetics by examining peer-reviewed articles and conference papers. By integrating governing physical laws into the loss function of a neural network, PINNs offer a mesh-free method in scientific computing. Records published between 2020 and 2025 were retrieved from the databases Scopus, Web of Science, and IEEE Xplore. The initial dataset comprised 500 records, from which 292 unique publications were identified. These were screened, yielding a final set of 139 publications that met predefined eligibility criteria. The analysis reveals growth in research activity, with a pronounced increase from 2022 onward. The literature predominantly addresses electrodynamic problems, employs feedforward neural network architectures, and adopts physics-only training. Two-dimensional problem formulations dominate, with three-dimensional formulations concentrated almost exclusively in electrodynamics, and no publications addressing electroquasistatics were identified. Contingency tables show that methodological choices are not independent of problem characteristics: medium selection correlates with physics regime, and architectural diversity increases with spatial dimensionality. Based on these findings, priorities for future work include: addressing the gap in electroquasistatics, extending three-dimensional formulations to static and quasistatic regimes, broader architectural experimentation in lower-dimensional settings, and increased integration of labeled data in static electromagnetics. To support methodological consistency and reproducibility, a reporting checklist for future PINN-based electromagnetics publications is proposed.
Schmeing et al. (Tue,) studied this question.