A numerical method for one-dimensional action functionals of photonic band-gap structures
Bibliographic record
Abstract
Photonic band gaps (PBG), photonic analogues of electronic semiconductor band gaps, have attracted much attention recently because of numerous potential applications in communications and computing. Aközbek and John (Phys. Rev. E, 57, 2287 (1998).) developed a variational model of such band gaps, using action functionals, where solitary waves are expanded in terms of a finite orthonormal basis. These expansions to finite order N converged to solitary waves. The nonlinear polynomial equations for the coefficients in the expansions, have nonunique solutions. Our paper, makes a study of the multiplicity of the solutions for one-dimensional photonic band-gap structures. It is found that the nonuniqueness grows dramatically with the order of the expansion N. We use homotopy, which continuously deforms the solutions of exactly solvable systems, into the solutions of the systems to be solved with new results in numeric algebraic geometry, such that all solutions are determined. We used Maple 7 to obtain the polynomial equations for the variational coefficients, extending Aközbek and John's approach. A homotopy-based package PHCpack was used to solve the systems for N ≤ 4 and a linearization-extrapolation method was developed to find real solutions for N ≥ 5. The results are compared with the exact soliton solutions and their convergence behavior is discussed. The interplay of geometrical, topological and variational methods is seen in these interesting physical band-gap structures. PACS Nos.: 42.65.Tg, 42.70.Qs, 02.30.Xx, 02.70.Wz
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".