Statistical models in electromagnetic field analysis: A Systematic review with implications for clean energy systems

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Original abstract

This review examines how uncertainty has been incorporated into electromagnetic field analysis through statistical models derived from Maxwell-based formulations. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020), we screened and analyzed 60 peer-reviewed journal and conference papers from Scopus, Web of Science, and IEEE Xplore, and grouped the literature into thirteen recurring model families. Random matrix theory appears most often, representing 31.7% of the corpus, followed by stochastic Green's function methods (16.7%), Monte Carlo simulation (15.0%), and stochastic Maxwell or random-field formulations (13.3%). These numbers show that the field is converging around a limited set of physically grounded approaches instead of remaining dispersed across isolated techniques. The reviewed studies consistently point to the importance of Hill's plane-wave representation, the cavity quality factor, and experimentally tested statistical descriptions for reverberation chambers and other wave-chaotic enclosures. Several issues remain open, especially the definition of the lowest usable frequency, the validity of ergodic assumptions, estimation of the Rician K-factor, and practical criteria for choosing between Monte Carlo and polynomial chaos methods. Comparison across studies indicates that the most suitable model depends on where uncertainty enters the problem, whether through enclosure geometry, material disorder, parameter tolerances, or measurement-based calibration. Although this is not an energy-specific survey, the reviewed methods are relevant to converter-rich energy systems, wireless power transfer, and inverter-dominated installations, where electromagnetic reliability, EMC compliance, and robust design increasingly depend on uncertainty-aware analysis.

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