Dynamic of Time-Independent and Time-Dependent Asymmetric Gross-Pitaevskii Equation Around Exceptional Point
Bibliographic record
Abstract
Systems operating at exceptional points (EPs) are highly sensitive to small perturbations, making it challenging to work near an EP. Eigenvalue analysis of the Gross-Pitaevskii Equation has shown that asymmetric nonlinearity can compensate for detuning effects. However, an experimentally feasible system based on asymmetric nonlinear coupled resonators has not yet been explored. Additionally, some intriguing features of such a system are hidden in time domain analysis, which is rarely investigated. In this study, we demonstrate this feature using a full-wave simulation of an asymmetric nonlinear coupled resonator based on the Finite-Element method in Comsol. The time-dependent analysis reveals that detuning can shift the system from PT-Symmetric to broken PT-Symmetric (or vice versa), and nonlinearity can reverse this dynamic. This study provides an experimental framework for examining exceptional points (EPs) in nonlinear detuned coupled resonators and opens up new avenues for fundamental research into the influence of nonlinearity and detuning on the system’s state during EP encirclement
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 | 0.000 |
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 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".