Quantifying complexity and noise induced order via information theory measures and ordinal patterns symbolic analysis

In the first part of the talk I will introduce the concepts of ordinal
patterns and statistical complexity and I will show how they can be employed
to detect subtle signatures of noise-induced order and complexity in
nonlinear systems. I will illustrate the methodology with two paradigmatic
models: a Brownian particle in a sinusoidally modulated bistable potential
and the FitzHugh-Nagumo model for excitable systems. I will show that
resonant-like behavior occurs, in the form of enhanced temporal order,
detected as a minimum of Shannon\'s entropy, accompanied by a maximum of the
statistical complexity [1]. In the second part of the talk I will discuss
how this methodology can be employed for the analysis of experimental data.
As a first example I will discuss the analysis of the time-series of the
output intensity of a semiconductor laser operating in the feedback-induced
low-frequency fluctuations (LFFs) regime; a second example will be the
analysis of climatological data (monthly global surface air temperature)
defining a climate complex network.

[1] O. A. Rosso and C. Masoller, Phys. Rev. E 79, 040106(R) (2009).