A Combined Inverse Source and Scattering Technique for Dielectric Profile Design to Tailor Electromagnetic Fields
Bibliographic record
Abstract
This article augments existing gradient-based inverse scattering algorithms to enable the design of reflectionless lossless permittivity profiles within a given design domain that can transform an input incident field into an output field of desired characteristics. These desired characteristics are often some user-defined far-field performance criteria, such as main beam directions, null directions, and half-power beamwidth (HPBW). To this end, two extra steps will be performed prior to the inverse scattering step. First, an inverse source algorithm inverts the desired far-field performance criteria to infer a set of equivalent surface currents on a boundary close to the design domain. These equivalent currents are then converted to a set of field values that constitute the required aperture fields. Second, these aperture fields are scaled such that the input incident power to the design domain is approximately equal to the output power leaving the design domain. Finally, the desired scattered fields are formed and then inverted by an inverse scattering algorithm to reconstruct a lossless reflectionless dielectric profile within the design domain. We also show that the inverse scattering algorithm can employ appropriate regularization methods, in particular a binary regularization term, to facilitate the physical implementation of reconstructed dielectric profiles.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".