Bifurcation analysis of domain-dependent nonlinear dynamics for a cross-diffusive reaction-diffusion model
Bibliographic record
Abstract
Abstract In this work, domain-dependent stability analysis is conducted for a reaction diffusion system with linear cross-diffusion. Its spatiotemporal dynamics are explored for the case of an activator-depleted model of two chemical species in terms of the domain size and its model parameters. Linear stability analysis is employed to derive the constraints which are necessary in understanding the role of linear cross-diffusion in driving the instability of the system. The conditions are proven in terms of lower and upper bounds of the domain size together with the reaction, self and cross-diffusion coefficients. The full parameter classification of the model system is presented in terms of the relationship between the domain size and cross-diffusion-driven instability. Subsequently, regions showing Turing instability, Hopf and transcritical types of bifurcations are demonstrated using the parameter values of the system. Theoretical findings are supported by the finite element simulations on two dimensional rectangular domain. Numerical simulations showing each of the three types of dynamics are examined for the Schnakenberg kinetics, also known as an activator-depleted reaction kinetics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".