Adaptive Time Domain Sparse Wavelet Approximations to Transient Space-Time Electromagnetic Wave Fields
Bibliographic record
Abstract
The purpose of this contribution is to introduce a method for constructing temporally adaptive, sparse, accurate, efficient and reliable representations of large scale, discretely sampled transient space-time electromagnetic wave fields, through the use of sparse wavelet approximations. The temporally adaptive sparse wavelet approximations are achieved by performing a three step computational process during a time domain electromagnetic simulation. The computational process consists of a forward Fast Wavelet Transform (FWT) step, an adaptive wavelet coefficient truncation step, and an inverse FWT step. The sparse approximation of a space-time electromagnetic wave field results in retaining wavelet coefficients with the largest magnitudes, localized in regions of the solution domain with high electromagnetic energy density concentrations. Numerical results that demonstrate the applicability and versatility of the approach are provided. Rigorous error analyses are also provided to demonstrate the accuracy of the method.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.032 | 0.007 |
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; both teacher heads agree on what is shown here.
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".