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==Dijkstra Algorithm and transfer matrix==
=Thermal Interfaces=


The dynamics is overdamped, so that we can neglect the inertial term. The Langevin equation of motion is
<center> <math>
\partial_t h(r,t)= - \mu \frac{\delta E_{pot}}{\delta h(r,t)} + \eta(r,t)
</math></center>
The first term <math> -  \delta E_{pot}/\delta h(r,t) </math> is the elastic force trying to smooth the interface, the mobility <math> \mu </math> is the inverse of the viscosity. The second term is the Langevin noise. It is Guassian and defined by
<center> <math>
\langle \eta(r,t) \rangle =0, \; \langle \eta(r',t')\eta(r,t) \rangle = 2 d D \delta^d(r-r') \delta(t-t')
</math></center>
The symbol <math> \langle \ldots \rangle</math> indicates the average over the thermal noise and the diffusion constant is fixed by the Einstein relation <math>
  D= \mu K_B T
</math>. We set  <math> \mu= K_B=1</math>


 
The potential energy of surface tension (<math>\nu </math> is the stiffness) can be expanded at the lowest order in the gradient:
[[File:SketchDPRM.png|thumb|left|Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left either one step right.  A random energy <math> V(\tau,x)</math> is associated at each node and the total energy is simply <math> E[x(\tau)] =\sum_{\tau=0}^t V(\tau,x)</math>. ]]
<center> <math>  
 
E_{pot} \sim \text{const.} + \frac{\nu}{2} \int d^d r (\nabla h)^2
 
</math></center>
We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm.
Hence, we have the Edwards Wilkinson equation:
 
Dijkstra allows to identify the minimal  energy among the exponential number of  configurations <math> x(\tau)</math>
<center> <math>
<center> <math>
E_{\min} = \min_{x(\tau)} E[x(\tau)].
\partial_t h(r,t)= \nu \nabla^2 h(r,t) + \eta(r,t)
</math></center>
</math></center>
 
=== Scaling Invariance===
We are also interested in the ground state configuration <math> x_{\min}(\tau) </math>.
The equation enjoys of a continuous symmetry because <math> h(r,t) </math> and <math> h(r,t)+c </math> cannot be distinguished. This is a condition of scale invariance:
For both quantities we expect scale invariance with two exponents  <math> \theta, \zeta</math> for the energy and for the roughness  
<center> <math>  
<center>
h(b r, b^z t) \overset{in law}{\sim} b^{\alpha} h(r,t)
<math>
</math></center>
E_{\min} = c_\infty t + b_\infty t^{\theta}\chi,   \quad x_{\min}(t/2)) \sim  a_\infty t^{\zeta} \tilde \chi
Here <math>  
z, \alpha
</math> are the dynamic and the roughness exponent respectively. From dimensional analysis
<center> <math>
b^{\alpha-z} \partial_t h(r,t)= b^{\alpha-2} \nabla^2 h(r,t) +b^{-d/2-z/2} \eta(r,t)
</math></center>
</math></center>
From which you get <math> z=2 </math> in any dimension and a rough interface below <math> d=2 </math> with <math> \alpha =(2-d)/2 </math>.


<strong>Universal exponents: </strong> Both  <math> \theta, \zeta </math> are  Independent of the lattice, the disorder distribution, the elastic constants, or the boudanry conditions.  Note that  <math> \omega= \theta </math>, while for an interface <math> \omega= d \theta </math>.
== Width of the interface ==


<strong>Non-universal constants: </strong> <math> c_\infty,b_\infty, a_\infty </math> are of  order 1 and depend on the  lattice, the disorder distribution, the elastic constants... However  <math> c_\infty </math> is independent on the boudanry conditions!
Consider a 1-dimensional line of size L with periodic boundary conditions.
We consider the width square of the interface
<center> <math>  
w_2(t) = \left[\int_0^L \frac{d r}{L} \left(h(r,t) - \int_0^L \frac{dr}{L} h(r,t)\right)\right]^2
</math></center>
It is useful to introduce the Fourier modes:
<center> <math>
\hat h_q(t)= \frac{1}{L} \int_0^L e^{iqr} h(r,t), \quad h(r,t)= \sum_q e^{-iqr} \hat h_q(t)
</math></center>
Here <math> q=2 \pi n/L, n=\ldots ,-1,0,1,\ldots</math> and recall <math> \int_0^L d r e^{iqr}= L \delta_{q,0} </math>.
using de Parseval theorem for the Fourier series
<center> <math>
w_2(t) = \sum_{q\ne 0} |\hat h_q(t)|^2  =\sum_{q\ne 0} \left(\hat h_q(t) \hat h_{-q}(t)\right) ^2  
</math></center>
In the last step we used that <math>  
\hat h_q^*(t)= \hat h_{-q}(t)
</math>.


<strong>Universal distributions: </strong> <math> \chi, \tilde \chi </math> are instead universal, but depends on the boundary condtions.  Starting from 2000 a magic connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular I discuss two different boundary conditions:
=== Solution in the Fourier space===
show that the  EW equation writes
<center> <math>  
\partial_t \hat h_q(t)= -\nu q^2 \hat h_q(t) + \eta_q(t), \quad \text{with} \; \langle \eta_{q_1}(t')  \eta_{q_2}(t)\rangle =\frac{2 T}{L} \delta_{q_1,-q_2}\delta(t-t') 
</math></center>


* <strong>Droplet</strong>: <math> x(\tau=0) = x(\tau=t) = 0 </math>. In this case, up to rescaling,  <math> \chi</math> is distributed as the smallest eigenvalue of a GUE random matrix (Tracy Widom distribution <math>F_2(\chi) </math>)
The solution of this first order linear equation writes
<center> <math>  
\hat h_q(t)= \hat h_q(0) e^{-\nu q^2  t} +\int_0^t d s e^{- \nu q^2 (t-s)} \eta_q(s)
</math></center>


* <strong> Flat</strong>: <math> x(\tau=0) = 0 </math> while the other end <math>  x(\tau=t) </math> is free. In this case, up to rescaling,  <math> \chi</math> is distributed as the smallest eigenvalue of a GOE random matrix (Tracy Widom distribution <math>F_1(\chi) </math>)
* Assume that the interface is initially flat, namely <math> \hat h_q(0) =0 </math>.  Show that
===Entropy and scaling relation===
<center> <math>  
\langle \hat h_q(t) \hat h_{-q}(t) \rangle =\begin{cases}
\dfrac{T(1 - e^{-2\nu q^{2}t})}{L \nu q^{2}}, & q \neq 0, \\[1.2em]
\frac{2 T}{L} t, & q = 0.
\end{cases}
</math></center>


It is useful to compute the entropy
*The mean width square grows at short times and saturates at long times:
<center>
<center> <math>  
<math>
\langle w_2(t)\rangle =  \dfrac{T}{L \nu }\sum_{q\ne 0} \dfrac{1 - e^{-2\nu q^{2}t}}{q^{2}} =\begin{cases}
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 2 -\frac{x^2}{t} +O(x^4)
T \sqrt{\frac{2 t}{\pi \nu}}, & t\ll L^2, \\[1.2em]
</math></center>
\frac{T}{ \nu} \frac{L}{12} , & t\gg L^2.
From which one could guess from dimensional analysis 
\end{cases}
<center>
<math>
\theta=2 \zeta-1
</math></center>
</math></center>
This relation is actually exact also for the continuum model.

Latest revision as of 04:44, 16 September 2025

Thermal Interfaces

The dynamics is overdamped, so that we can neglect the inertial term. The Langevin equation of motion is

The first term is the elastic force trying to smooth the interface, the mobility is the inverse of the viscosity. The second term is the Langevin noise. It is Guassian and defined by

The symbol indicates the average over the thermal noise and the diffusion constant is fixed by the Einstein relation . We set

The potential energy of surface tension ( is the stiffness) can be expanded at the lowest order in the gradient:

Hence, we have the Edwards Wilkinson equation:

Scaling Invariance

The equation enjoys of a continuous symmetry because and cannot be distinguished. This is a condition of scale invariance:

Here are the dynamic and the roughness exponent respectively. From dimensional analysis

From which you get in any dimension and a rough interface below with .

Width of the interface

Consider a 1-dimensional line of size L with periodic boundary conditions. We consider the width square of the interface

It is useful to introduce the Fourier modes:

Here and recall . using de Parseval theorem for the Fourier series

In the last step we used that .

Solution in the Fourier space

show that the EW equation writes

The solution of this first order linear equation writes

  • Assume that the interface is initially flat, namely . Show that
  • The mean width square grows at short times and saturates at long times: