SuperVoxHenry: Tucker-Enhanced and FFT-Accelerated Inductance Extraction for Voxelized Superconducting Structures
Why this work is in the frame
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Bibliographic record
Abstract
This article introduces SuperVoxHenry, an inductance extraction simulator for analyzing voxelized superconducting structures. SuperVoxHenry extends the capabilities of the inductance extractor VoxHenry for analyzing the superconducting structures by incorporating the following enhancements. First, SuperVoxHenry utilizes a two-fluid model to account for normal currents and supercurrents. Second, SuperVoxHenry introduces the Tucker decompositions to reduce the memory requirement of circulant tensors as well as the setup time of the simulator. Finally, SuperVoxHenry incorporates an aggregation-based algebraic multigrid technique to obtain the sparse preconditioner. With these enhancements, SuperVoxHenry allows extracting the inductance of large-scale superconducting structures on a desktop computer. The accuracy, efficiency, and applicability of the proposed SuperVoxHenry have been demonstrated through the inductance extraction of various superconducting structures, including superconducting thin film inductors, a sharp bend, as well as a subsystem of an energy-efficient single flux quantum circuit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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