Reinforcement Learning for Topology Optimization of a Synchronous Reluctance Motor
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Bibliographic record
Abstract
In this article, a method for topology optimization (TO) of a synchronous reluctance motor (SynRM) is proposed using deep reinforcement learning (RL). Due to the need for simulating a large number of finite-element models in a traditional TO task, incorporating a study involving a different problem formulation (such as a varying design domain) can be an overwhelming task. A neural network (NN)-based agent trained using an RL formulation is able to extend the knowledge from one TO design problem to other similar TO tasks. The applicability of such learning is performed using a sequence-based TO environment. It is observed that such an approach not only reduces the computation required for TO, but also introduces the capability to generalize RL to unseen TO scenarios. The proposed optimization method reduces computation time by 70%–90% when compared to a genetic algorithm-based implementation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it