Performance comparison of TLM, FDTD and Haar wavelet MRTD algorithms for electromagnetic simulations
Bibliographic record
Abstract
The performance in computation time, computer memory and computation accuracy of the TLM, FDTD, 0-0rder and 1st-Order Haar wavelet MR.TD algorithms have been tested. The results show: (1) The FDTD algorithm is about 1.25 times as fast as TLM. The CPU runtimes for the 0- and 1st-Order MR.TD are almost the same as that of FDTD although much less time steps are required in the 0- and 1st-Order MRTD for the same time duration. The CPU runtimes linearly vary with the time step. Thus the CPU runtime of each algorithm is mainly determined by the number of variables that must be retrieved from, and stored in memory at each time step; (2) Because of the numerical comparison and memory reallocation in each time step, thresholding greatly increases computing load; (3) The 1st-Order Haar wavelet MRTD can save much more memory. The optimal relative threshold fraction is about 0.01 % and large memory savings are attainable while maintaining reasonable accuracy. But the CPU runtimes are too long for the 0-0rder and 1st-Order MRTD with thresholding technique. Thus the tradeoff between computational efficiency and memory saving should be taken into consideration. An optimum procedure and well-designed data structures are necessary to ensure that memory requirements are kept at a minimum while maintaining computational efficiency at the same time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".