TBan-II: Difference between revisions

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=Directed Polymers on a lattice=
=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. Dijkstra allows to identify the minimal  energy among the exponential number of  configurations <math> x(\tau)</math>
Hence, we have the Edwards Wilkinson equation:
<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>
=== Scaling Invariance===
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:
<center> <math>
h(b r, b^z t) \overset{in law}{\sim}  b^{\alpha} h(r,t)
</math></center>
</math></center>
 
Here <math>  
We are also interested in the ground state configuration  <math> x_{\min}(\tau) </math>.
z, \alpha
For both quantities we expect scale invariance with two exponents  <math> \theta, \zeta</math> for the energy and for the roughness  
</math> are the dynamic and the roughness exponent respectively. From dimensional analysis
<center>
<center> <math>
<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)
E_{\min} = c_\infty t + b_\infty t^{\theta}\chi,   \quad x_{\min}(t/2)) \sim  a_\infty t^{\zeta} \tilde \chi
</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. 
== 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

th(r,t)=μδEpotδh(r,t)+η(r,t)

The first term δEpot/δh(r,t) is the elastic force trying to smooth the interface, the mobility μ is the inverse of the viscosity. The second term is the Langevin noise. It is Guassian and defined by

η(r,t)=0,η(r,t)η(r,t)=2dDδd(rr)δ(tt)

The symbol indicates the average over the thermal noise and the diffusion constant is fixed by the Einstein relation D=μKBT. We set μ=KB=1

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

Epotconst.+ν2ddr(h)2

Hence, we have the Edwards Wilkinson equation:

th(r,t)=ν2h(r,t)+η(r,t)

Scaling Invariance

The equation enjoys of a continuous symmetry because h(r,t) and h(r,t)+c cannot be distinguished. This is a condition of scale invariance:

h(br,bzt)inlawbαh(r,t)

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

bαzth(r,t)=bα22h(r,t)+bd/2z/2η(r,t)

From which you get z=2 in any dimension and a rough interface below d=2 with α=(2d)/2.

Width of the interface

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

w2(t)=[0LdrL(h(r,t)0LdrLh(r,t))]2

It is useful to introduce the Fourier modes:

h^q(t)=1L0Leiqrh(r,t),h(r,t)=qeiqrh^q(t)

Here q=2πn/L,n=,1,0,1, and recall 0Ldreiqr=Lδq,0. using de Parseval theorem for the Fourier series

w2(t)=q0|h^q(t)|2=q0(h^q(t)h^q(t))2

In the last step we used that h^q*(t)=h^q(t).

Solution in the Fourier space

show that the EW equation writes

th^q(t)=νq2h^q(t)+ηq(t),withηq1(t)ηq2(t)=2TLδq1,q2δ(tt)

The solution of this first order linear equation writes

h^q(t)=h^q(0)eνq2t+0tdseνq2(ts)ηq(s)
  • Assume that the interface is initially flat, namely h^q(0)=0. Show that
h^q(t)h^q(t)={T(1e2νq2t)Lνq2,q0,2TLt,q=0.
  • The mean width square grows at short times and saturates at long times:
w2(t)=TLνq01e2νq2tq2={T2tπν,tL2,TνL12,tL2.