Application of artificial neural networks for electromagnetic modeling and computational electromagnetics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an overview of emerging artificial neural network (ANN) techniques and applications for electromagnetic (EM) simulation and design. Accurate time domain EM modeling using recurrent neural networks (RNNs) is reviewed. Advanced robust training algorithm combining particle swarm optimization (PSO) and quasi-Newton method is described through frequency domain EM modeling, showing its ability to avoid ANN training being trapped in local minima to obtain accurate models. ANN applications in computational electromagnetics are also discussed. Great efficiency can be achieved by using ANNs to approximate the computationally intensive calculations in solving Maxwell equations using method of moments (MoM). As illustrated in examples, these ANN-based techniques are capable of fast and accurate EM modeling and MoM computation, and useful for efficient EM based design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it