L-6

From Disordered Systems Wiki
Jump to navigation Jump to search

Avalanches and Bienaymé-Galton-Watson process

Goal: We solve the mean field version of the cellular automaton, derive its avalanche statistics and make a connection with the Bienaymé-Galton-Watson process used to describe an epidemic outbreak.

Fully connected (mean field) model for the cellular automaton

Let's study the mean field version of the cellular automata introduced in the previous lecture. We introduce two approximations:

  • Replace the Laplacian, which is short range, with a mean field fully connected interction

.


  • The local threshold are all equal. In particular we set

.


As a consequence, in the limit , the statistical properties of the system are described by the distribution of the local stresses . For simplicity, instead of the stresses, we study the distance from threshold

Our goal is thus to determine their distribution , given their intial distribution, , and a value of .

Dynamics

Let's rewrite the dynamics with the new variables

  • Drive: Increasing each point decreases its distance to threshold

.

As a consequence


  • Instability 1: Stress drop The instability occurs when a point is at . Then, the point is stabilized (stress drop):

Increasing Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle w \to w + dw} , a fraction Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle m^2 d w P_w(0) } of the blocks is unstable. Due to the stress drop, their distance to threshold becomes . Hence, one writes

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \partial_w P_{w}(x) \sim m^2 \left[\partial_x P_w(x) + P_w(0) g(x) \right] }


  • Instability 2: Stress redistribution The stress drop of a single block induces a stress redistribution where all blocks approach threshold.
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x_i \to = x_i +\frac{1}{L} \frac{\Delta}{1+m^2} }

The total stress drop is hence all points move to the origin of

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle m^2 dw P_w(0) \frac{\overline{\Delta}}{1+m^2} }

part of them shifts, part of them become unstable... we can write

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \partial_w P_{w}(x) = m^2 \left[\partial_x P_w(x) + P_w(0) g(x) \right] \left[ 1+P_w(0) \frac{\overline{\Delta}}{1+m^2} + (P_w(0) \frac{\overline{\Delta}}{1+m^2})^2 +\ldots\right] }

and finally:

Stationary solution

Increasing the drive the distribution converge to the fixed point:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 0 = \partial_x P_{\text{stat}}(x) + P_{\text{stat}}(0) g(x) }
  • Determne Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P_{\text{stat}}(0) =\frac{1}{\overline{\Delta}} } using
  • Show
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P_{\text{stat}}(x)= \frac{1}{\overline{\Delta}} \int_x^\infty g(z) d z }

which is well normalized.

Critical Force

The average distance from the threshold gives a simple relation for the critical force, namely Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 1-f_c= \overline{x} } . Hence for the automata model we obtain:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f_c= 1- \int_0^\infty d x x P_{\text{stat}}(x)= 1 - \frac{1}{2}\frac{\overline{\Delta^2}}{\overline{\Delta}} }

Let's assume an exponential distribution of the thresholds and show Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle P_{\text{stat}}(x)= e^{-x/\overline{\Delta}}/\overline{\Delta} }