Computational aspects in modelling electromagnetic field parameters in microstrips.
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 thesis introduces the results of a detailed investigation and analysis into the key aspects of the Vector Finite Element Method, the critical relationships between the VFEM zone discretization and the associated solution matrix equations, the discovery of the original fill-in laws and the fill-in prediction methods, and their impact on the computational aspects and processes. Furthermore, the work included the design and implementation of accelerated matrix solution models, and their successful implementation and application to various electromagnetic problems and the presentation of the excellent performance results obtained. Also, one of the problem examples used in the investigation contributed to the validation of a method to reduce the characteristic impedance of a microstrip by means of grooves. Finally, the modelling of the electromagnetic problems by Artificial Neural Networks, and the successful investigation of an original concept---training by decimation---led to the validation of Artificial Neural Networks as a real-time modelling tool which completed the work of this thesis.
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