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Created page with "= Avalanches and BGW process= = Fully connected model foor the cellular automata (mean field)= Let's study the mean field version of the cellular automata introduced in the previous lecture. * The elastic coupling is with all neighbours <center><math> \sigma_i= h_{CM} - h_i + m^2(w-h_i), \quad </math></center>. * The local random threshold are all equal: <center> <math> \sigma_i^{th}=1, \quad \forall i </math></center>. Instead of following the evoluion..."
 
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= Avalanches and BGW process=
= Avalanches and Bienaymé-Galton-Watson process=
 
<Strong> Goal: </Strong> 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 model foor the cellular automata (mean field)=
= Fully connected model foor the cellular automata (mean field)=

Revision as of 15:09, 29 February 2024

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 model foor the cellular automata (mean field)

Let's study the mean field version of the cellular automata introduced in the previous lecture.

  • The elastic coupling is with all neighbours
σi=hCMhi+m2(whi),

.

  • The local random threshold are all equal:
σith=1,i

.



Instead of following the evoluion of the σi, it is useful to introduce the distance from threshold
xi=1σi

Hence, an unstable point, xi<0, is stabilized to a value x>0 drawn from g(x). The stress redistribution induced on each bloch is 1Lx1+m2


In the limit L we define the distribution Pw(x) and write its evolution equation.

  • Drive: Changing ww+dw gives
    Pw+dw(x)=Pw(x+dw)Pw(x)+m2dwxPw(x)
  • Instability: This shift is stable far from the origin, however for a fraction m2dwPw(0) of the points of the interface is unstable. Due to the stress drop, their distance to instability will be m2dwPw(0)g(x). Hence, one writes
wPw(x)m2[xPw(x)+Pw(0)g(x)]
  • Stress redistribution: as a consequence all points move to the origin of
m2dwPw(0)x1+m2

where x=dxxg(x)

  • Avalanche: Let us call we can write
wPw(x)=m2[xPw(x)+Pw(0)g(x)][1+Pw(0)x1+m2+(Pw(0)x1+m2)2+]

and finally:

wPw(x)=m21Pw(0)x1+m2[xPw(x)+Pw(0)g(x)]

Stationary solution

Increasing the drive the distribution converge to the fixed point:

0=xPstat(x)+Pstat(0)g(x)
  • Determne Pstat(0)=1x using
1=0dxPstat(x)=0dxxxPstat(x)
  • Show
Pstat(x)=1xxg(z)dz