301 research outputs found
Wavelength selection beyond Turing
Spatial patterns arising spontaneously due to internal processes are
ubiquitous in nature, varying from regular patterns of dryland vegetation to
complex structures of bacterial colonies. Many of these patterns can be
explained in the context of a Turing instability, where patterns emerge due to
two locally interacting components that diffuse with different speeds in the
medium. Turing patterns are multistable, such that many different patterns with
different wavelengths are possible for the same set of parameters, but in a
given region typically only one such wavelength is dominant. In the Turing
instability region, random initial conditions will mostly lead to a wavelength
that is similar to that of the leading eigenvector that arises from the linear
stability analysis, but when venturing beyond, little is known about the
pattern that will emerge. Using dryland vegetation as a case study, we use
different models of drylands ecosystems to study the wavelength pattern that is
selected in various scenarios beyond the Turing instability region, focusing
the phenomena of localized states and repeated local disturbances
Spatial pattern formation induced by Gaussian white noise
The ability of Gaussian noise to induce ordered states in dynamical systems
is here presented in an overview of the main stochastic mechanisms able to
generate spatial patterns. These mechanisms involve: (i) a deterministic local
dynamics term, accounting for the local rate of variation of the field
variable, (ii) a noise component (additive or multiplicative) accounting for
the unavoidable environmental disturbances, and (iii) a linear spatial coupling
component, which provides spatial coherence and takes into account diffusion
mechanisms. We investigate these dynamics using analytical tools, such as
mean-field theory, linear stability analysis and structure function analysis,
and use numerical simulations to confirm these analytical results.Comment: 11 pages, 8 figure
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