Application of Artificial Intelligence Techniques on Computational Electromagnetics for Power System Apparatus: An Overview
Bibliographic record
Abstract
This paper provides a review of the most recent advances in artificial intelligence (AI) as applied to computational electromagnetics (CEM) to address challenges and unlock opportunities in power system applications. It is intended to provide readers and practitioners in electromagnetics (EM) and related applicable fields with valuable perspectives on the efficiency and capabilities of machine learning (ML) techniques used with CEM tools, offering unparalleled computational advantage. The discussion begins with an overview of traditional computational methods in EM, highlighting their strengths and limitations. The paper then delves into the integration of AI techniques, including ML, deep learning, and optimization algorithms, into CEM frameworks. Emphasis is placed on how AI enhances the accuracy and efficiency of EM simulations, enabling rapid analysis and optimization of power system components and configurations. Case studies and examples illustrate the successful application of AI-based CEM in solving practical challenges in electrical machine modeling, condition monitoring, and design optimizations in power systems. This paper conducts a comprehensive assessment of AI-based CEM techniques, critically evaluating their merits, addressing open issues, and examining the technical implementations within the context of power system applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".