Matrix-Free Nonlinear Finite-Element Solver Using Transmission-Line Modeling on GPU
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The transmission-line modeling (TLM) used for nonlinear finite-element (FE) solution has a paramount feature that the admittance matrix is unchanged and only needs one-time factorization; and this feature becomes a drawback when the required number of TLM iterations increase due to the mismatch between the transmission-line impedance and the load. In this paper, a matrix-free TLM scheme is proposed to make use of the solved nonlinear reluctivities without employing any matrices at each timestep, thus substantially decreasing the number of required TLM iterations. The matrix-free solver is suitable for massively parallel processing and the design is implemented on the Tesla V100 graphics processing unit (GPU). A speedup of more than 27 times is obtained compared with a commercial FE package for different problem sizes while maintaining high accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 it