On the analytic and approximate solutions for the fractional nonlinear Schrödinger equations
Bibliographic record
Abstract
In this work, we are devoted to the following fractional nonlinear Schrödinger equation with the initial conditions in the Caputo sense for \(1 < \alpha \leq 2\): \begin{equation}\label{J1} \begin{cases} \displaystyle i \frac{ _c \partial^{\alpha}}{\partial \theta^{\alpha}} W(\theta, \sigma) + \beta_1 \frac{ \partial^{2}}{\partial \sigma^{2}} W(\theta, \sigma) \\ \hspace{0.5in} + \gamma (\theta, \sigma) W(\theta, \sigma) + \beta_2 |W(\theta, \sigma)|^2 W (\theta, \sigma) + \beta_3 W^2 (\theta, \sigma) = 0,\\ W(0, \sigma) = \phi_1(\sigma), \quad W_\theta'(0, \sigma) = \phi_2(\sigma), \end{cases} \end{equation} where \(\theta > 0, \sigma \in \mathbb{R}\), \(\gamma(\theta, \sigma)\) is a continuous function and \(\beta_1, \beta_2, \beta_3\) are constants. Our analysis for deriving analytic and approximate solutions to the Schrödinger equation relies on the Adomian decomposition method and fractional calculus. Several illustrative examples are presented to demonstrate the solution constructions. Finally, the variant and symmetric system of the fractional nonlinear Schrödinger equations are studied.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".