On incorporating finite impulse response neural network with finite difference time domain method for simulating electromagnetic problems
Why this work is in the frame
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Bibliographic record
Abstract
The finite difference time domain method (FDTD) is a very powerful numerical method to solve electromagnetic (EM) problems. It is very flexible to simulate the problems which have very complex boundaries. It is well known that FDTD method requires long computation time for solving the resonant or high-Q passive structures. The reason for this is because the algorithm is based on the leap-frog formula. For EM modeling is very important to speed up the simulation. The finite impulse response neural network (FIR NN) is applied as a nonlinear predictor to predict time series signal for speeding up the FDTD simulations. The FIR NN is trained by temporal backpropagation learning algorithm. A waveguide filter is used as an example and simulated by the FDTD method. It demonstrates that a short segment of an FDTD data is used to train the predictor, and the predictor can predict later information very well. The total least square (TLS) method is used as a predictor as well. By comparing the predicted error, it is shown that FIR neural network gives better prediction than that of the TLS.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 it