Computationally Efficient Flux Linkage Estimation of PMaSynRMs Using Physics-Informed Neural Networks
Bibliographic record
Abstract
Synchronous reluctance machines (SynRMs) and permanent magnet-assisted SynRMs (PMaSynRMs) are promising rare-earth-free alternatives to permanent magnet synchronous machines (PMSMs), which suffer from high cost and supply chain issues. PMaSynRMs may be operated with control strategies that optimize the operation in maximum torque per ampere (MTPA) and field weakening (FW) regions. This goal is achieved with accurate estimation of parameters such as d- and qaxis flux linkages and incorporating them in the control process. Finite element analysis (FEA) offers accurate parameter values but is computationally intensive, while artificial neural networks (ANN) demand large datasets for reliable accuracy. To tackle these challenges, this paper proposes and implements a state-of-the-art physics-informed neural network (PINN) framework to estimate the d- and q-axis flux linkages of PMaSynRMs. The study highlights the burden of obtaining FEA-based lookup tables and compares the proposed PINN framework with ANN framework. Results demonstrate that PINN can accurately estimate flux linkages despite the highly anisotropic structure of PMaSynRMs, while using a reduced number of data samples compared to ANN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".