Fast Prediction of Transport Structures in the Melt by Physics Informed Neural Networks during ‘VMCz’ Crystal Growth of Silicon
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Bibliographic record
Abstract
Fast prediction of fluid flow and thermal fields during the growth of bulk silicon single crystals by the ‘Vertical Magnetic Field Applied Czochralski (VMCz) Method’ was successfully achieved by the application of Physics Informed Neural Networks (PINNs) without any answer-labeled training data generated by a numerical simulation. The PINNs’ results are in good agreement with those of the numerical simulation. The prediction time by PINNs was significantly reduced; to less than 0.1 seconds compared with about 30 minutes required by the numerical simulation. Moreover, being mesh-free techniques, PINNs do not require mesh reconstruction to accommodate the change in the growth melt volume during growth. This shows that PINNs have great potential, as real-time simulation techniques, for future applications in various areas.
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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) | 0.000 | 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