Feed-forward neural network surrogate model for rapid simulations of a high-temperature superconducting bulk undulator
Bibliographic record
Abstract
Abstract This work presents the use of a feed-forward neural network (FFNN) surrogate model to significantly increase the speed of simulation time for a meter-long high-temperature superconducting bulk undulator. We generate a dataset of the undulator with bulks of different critical current densities using the finite element method (FEM), which is then used to train an FFNN. We show that the FFNN can output the results of interest of the full finite element model to an accuracy of 0.28%, while requiring a computation time of 200 ms instead of 5.7 h with FEM. Finally, we use the FFNN to develop an inverse analysis to estimate the critical current density of each bulk from a given undulator field and show that this procedure can reproduce the critical current density to within 0.47% deviation in 12 iterations and 2.5 s. If the full FEM simulations were used, the computation times would be ∼68 h, demonstrating a speed increase of nearly 100 000.
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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 | 0.002 |
| 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 | 0.001 | 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".