AH FDTD Method and Its Application in Solving Electromagnetic Forward and Inverse Problems Based on Machine Learning
Bibliographic record
Abstract
In this letter, we explore the feasibility of using machine learning (ML) to address time-domain electromagnetic forward and inverse problems. A novel ML-associated Hermite (AH) finite-difference time-domain (FDTD) solution method is proposed, which utilizes datasets generated by the AH FDTD method to train the neural network model, and then uses this trained model for prediction. Both the prediction of the target scattering field and the inversion of the target can achieve good results. Moreover, in the task of predicting the locations of metal tunnels, the frequency-domain convolutional neural network performs better than Galerkin Transformer, and simulation of this case using AH FDTD method, the required dataset can be obtained in about 2 h, whereas FDTD takes nearly 41 h.
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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".