On incorporating signal processing and neural network techniques with the FDTD method for solving electromagnetic problems
Bibliographic record
Abstract
The finite difference time domain method (FDTD) is a very powerful numerical method for solving electromagnetic (EM) problems. Due to its flexibility, it can be used to solve problems which have very complex boundaries. It is well known that the FDTD method requires long computation times for simulating the resonant or high-q structures. The reason for this is because the algorithm is based on the leap-frog formula. The quest to find a good predictor for enhancing the method is very interesting topic in EM modelling. In this paper, the autoregressive model (AR) and backpropagation neural network are designed as predictors. The total least squares (TLS) method is applied to obtain AR coefficients. A waveguide filter is used as an example and modeled using the FDTD method. We demonstrate that a short segment of an FDTD data set can be used to train the predictors and the predictors can predict later information very well.
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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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".