Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique

The problem with the analysis of noise-induced transitions between patterns in distributed stochastic systems is considered. As a key model, we use spatially extended dynamical “phytoplankton-herbivore” system diffusion. We perform parametric bifurcation this model and determine Turing instability zone, where non-homogeneous are generated by multistability deterministic coexistence several waveform pattern–attractors found. study how noise affects these estimate dispersion random states using new technique based on sensitivity function (SSF) confidence domain method. To investigate preferences patterns, analyze compare results theoretical approach statistics extracted from direct numerical simulation.