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<strong>Goal:</strong> This lecture is dedicated to a classical model in disordered systems: the directed polymer in random media. It has been introduced to model vortices in superconductors or domain walls in magnetic films. We will focus on algorithms that identify the ground state or compute the free energy at temperature <math>T</math>, as well as on the Cole–Hopf transformation that maps this model to the KPZ equation.


<strong>Goal: </strong> This lecture is dedicated to a classical model in disordered systems: the directed polymer in random media. It has been introduced to model vortices in superconductur or domain wall in magnetic film. We will focus here on the algorithms that identify the ground state or compute the free energy at temperature T, as well as, on the Cole-Hopf transformation that map this model on the KPZ equation. 
= Directed Polymers (''d = 1'') =


= Directed Polymers (''d = 1'')=
The configuration is described by a vector function <math>\vec{x}(t)</math>, where <math>t</math> is the internal coordinate. The polymer lives in <math>D = 1 + N</math> dimensions.
The configuration is described by a vector function:
<math>\vec{x}(t)</math>,
where <math>t</math> is the internal coordinate. The polymer lives in <math>D = 1 + N</math> dimensions.


Examples: vortex lines, DNA strands, fronts.
Examples: vortex lines, DNA strands, fronts.


Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.
Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.


=Directed Polymers on a lattice=
= Directed Polymers on a lattice =


[[File:SketchDPRM.png|thumb|left|Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left or one step right. A random energy <math>V(\tau,x)</math> is associated to each node and the total energy is simply <math>E[x(\tau)] = \sum_{\tau=0}^t V(\tau,x)</math>.]]


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 one to identify the minimal energy among the exponential number of configurations <math>x(\tau)</math>:
<math display="block">
E_{\min} = \min_{x(\tau)} E[x(\tau)].
</math>


[[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>. ]]
We are also interested in the ground state configuration <math>x_{\min}(\tau)</math>. For both quantities we expect scale invariance with two exponents <math>\theta</math>, <math>\zeta</math> for the energy and for the roughness:
<math display="block">
E_{\min} = c_\infty t + \kappa_1 t^{\theta}\chi,
\quad
x_{\min}(t/2) \sim \kappa_2 t^{\zeta}\tilde\chi.
</math>


<strong>Universal exponents:</strong> Both <math>\theta</math> and <math>\zeta</math> are independent of the lattice, the disorder distribution, the elastic constants, or the boundary conditions.


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>
<strong>Non-universal constants:</strong> <math>c_\infty</math>, <math>\kappa_1</math>, <math>\kappa_2</math> are of order 1 and depend on the lattice, the disorder distribution, the elastic constants, etc. However <math>c_\infty</math> is independent of the boundary conditions.
<center> <math>
E_{\min} = \min_{x(\tau)} E[x(\tau)].  
</math></center>


We are also interested in the ground state configuration  <math> x_{\min}(\tau) </math>.
<strong>Universal distributions:</strong> <math>\chi</math>, <math>\tilde\chi</math> are universal, but depend on the boundary conditions. Starting from 2000, a remarkable connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular, we discuss two different boundary conditions:
For both quantities we expect scale invariance with two exponents  <math> \theta, \zeta</math> for the energy and for the roughness
<center>
<math>
E_{\min} = c_\infty t + \kappa_1 t^{\theta}\chi,  \quad x_{\min}(t/2)) \sim  \kappa_2 t^{\zeta} \tilde \chi
</math></center>


<strong>Universal exponents: </strong> Both  <math> \theta, \zeta </math> are  Independent of the lattice, the disorder distribution, the elastic constants, or the boudanry conditions.
* <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>).


<strong>Non-universal constants: </strong> <math> c_\infty,\kappa_1, \kappa_2 </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!
* <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>).


<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:
=== Entropy and scaling relation ===


* <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>)
It is useful to compute the entropy
<math display="block">
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 2 -\frac{x^2}{2 t} + O(x^4).
</math>
From which one could guess from dimensional analysis
<math display="block">
\theta = 2\zeta - 1.
</math>
This relation is actually exact also for the continuum model.


* <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>)
= Directed polymers in the continuum =
===Entropy and scaling relation===


It is useful to compute the entropy
We now reanalyze the previous problem in the presence of quenched disorder. Instead of discussing the case of interfaces, we will focus on directed polymers.
<center>
<math>
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 2 -\frac{x^2}{2 t} +O(x^4)
</math></center>
From which one could guess from dimensional analysis 
<center>
<math>
\theta=2 \zeta-1
</math></center>
This relation is actually exact also for the continuum model.


=Directed polymers in the continuum=
Let us consider polymers <math>x(\tau)</math> of length <math>t</math>. The energy associated with a given polymer configuration can be written as
We now reanalyze the previous problem in the presence of quenched disorder. 
<math display="block">
Instead of discussing the case of interfaces, we will focus on directed polymers. 
E[x(\tau)] = \int_0^t d\tau \left[ \frac{1}{2}\left(\frac{dx}{d\tau}\right)^2 + V(x(\tau),\tau) \right].
Let us consider polymers <math>x(\tau)</math> of length <math>t</math>.
The energy associated with a given polymer configuration can be written as
<center>
<math>
E[x(\tau)] = \int_0^t d\tau \, \left[ \frac{1}{2} \left( \frac{dx}{d\tau} \right)^2 + V(x(\tau), \tau) \right]
</math>
</math>
</center>
The first term describes the elastic energy of the polymer, while the second one is the disordered potential, which we assume to be
The first term describes the elastic energy of the polymer,
<math display="block">
while the second one is the disordered potential, which we assume to be
\overline{V(x,\tau)} = 0,
<center>
\qquad
<math>
\overline{V(x,\tau)V(x',\tau')} = D\,\delta(x-x')\,\delta(\tau-\tau').
\overline{ V(x,\tau) } = 0, \qquad
\overline{ V(x,\tau) V(x',\tau') } = D \, \delta(x-x') \, \delta(\tau-\tau') .
</math>
</math>
</center>
where <math>D</math> is the disorder strength.
where 'D' is the disorder strength.  


== Polymer partition function and propagator of a quantum particle ==


== Polymer partition function and propagator of a quantum particle==
Let us consider polymers starting at <math>0</math>, ending at <math>x</math> and at thermal equilibrium at temperature <math>T</math>. The partition function of the model reads
<math display="block">
Z(x,t) = \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
\exp\!\left[-\frac{1}{T}\int_0^t d\tau\left(\frac{1}{2}(\partial_\tau x)^2 + V(x(\tau),\tau)\right)\right].
</math>
Here, the partition function is written as a sum over all possible paths, corresponding to all polymer configurations that start at <math>0</math> and end at <math>x</math>, weighted by the appropriate Boltzmann factor.


Let us consider polymers starting in  <math>0 </math>, ending in <math>x </math> and at thermal equilibrium at  temperature <math>T</math>. The partition function of the model writes as
Let's perform the following change of variables: <math>\tau = i t'</math>. We also identify <math>T</math> with <math>\hbar</math> and <math>\tilde t = - i t</math> as the time.
<center> <math>
<math display="block">
Z(x,t) =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 +V(x(\tau),\tau)\right]
Z(x,\tilde t) = \int_{x(0)=0}^{x(\tilde t)=x} \mathcal{D}x(t')\,
</math></center>
\exp\!\left[\frac{i}{\hbar}\int_0^{\tilde t} dt'\left(\frac{1}{2}(\partial_{t'} x)^2 - V(x(t'),t')\right)\right].
Here, the partition function is written as a sum over all possible paths, corresponding to all possible polymer configurations that start at <math>0</math> and end at <math>x</math>, weighted by the appropriate Boltzmann factor.
</math>


Note that <math>S[x] = \int_0^{\tilde t} dt'\left(\frac{1}{2}(\partial_{t'} x)^2 - V(x(t'),t')\right)</math> is the classical action of a particle with kinetic energy <math>\frac{1}{2}(\partial_{t'}x)^2</math> and time-dependent potential <math>V(x(t'),t')</math>, evolving from time zero to time <math>\tilde t</math>. From the Feynman path integral formulation, <math>Z(x,\tilde t)</math> is the propagator of the quantum particle.


Let's perform the following change of variables: <math>\tau=i t' </math>. We also identifies <math>T</math> with <math>\hbar</math> and <math> \tilde t= -i t </math> as the time.
=== Feynman–Kac formula ===
<center> <math>
Z(x,\tilde t) =\int_{x(0)=0}^{x(\tilde t)=x} {\cal D} x(t') \exp\left[ \frac{i}{\hbar} \int_0^{\tilde t} d t' \frac{1}2(\partial_{t'} x)^2 -V(x(t'),t')\right]
</math></center>


Note that <math>  S[x]=  \int_0^{\tilde t} d t' \frac{1}2(\partial_{t'} x)^2 -V(x(t'),t')</math> is the classical action of a particle with kinetic energy  <math> \frac{1}2(\partial_\tau x)^2</math> and  time dependent potential <math> V(x(\tau),\tau)</math>, evolving from time zero to time <math> \tilde t</math>.
Let's derive the Feynman–Kac formula for <math>Z(x,t)</math> in the general case:
From the Feymann path integral formulation, <math> Z[x,\tilde t]</math>  is the propagator of the quantum particle.


* First, focus on free paths and introduce the following probability
<math display="block">
P[A,x,t] = \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
\exp\!\left[-\frac{1}{T}\int_0^t d\tau\,\frac{1}{2}(\partial_\tau x)^2\right]\,
\delta\!\left(\int_0^t d\tau\,V(x(\tau),\tau) - A\right).
</math>
* Second, the moment generating function
<math display="block">
Z_p(x,t) = \int_{-\infty}^{\infty} dA\,e^{-pA}P[A,x,t]
= \int_{x(0)=0}^{x(t)=x} \mathcal{D}x(\tau)\,
\exp\!\left[-\frac{1}{T}\int_0^t d\tau\,\frac{1}{2}(\partial_\tau x)^2
- p\int_0^t d\tau\,V(x(\tau),\tau)\right].
</math>
* Third, consider free paths evolving up to <math>t+dt</math> and reaching <math>x</math>:
<math display="block">
Z_p(x,t+dt)
= \left\langle e^{-p\int_0^{t+dt} d\tau\,V(x(\tau),\tau)} \right\rangle
= \left\langle e^{-p\int_0^{t} d\tau\,V(x(\tau),\tau)} \right\rangle e^{-pV(x,t)dt}
= [1-pV(x,t)dt+\dots]\left\langle Z_p(x-\Delta x,t)\right\rangle_{\Delta x}.
</math>
Here <math>\langle\cdots\rangle</math> is the average over all free paths, while <math>\langle\cdots\rangle_{\Delta x}</math> is the average over the last jump, namely <math>\langle\Delta x\rangle=0</math> and <math>\langle\Delta x^2\rangle = T\,dt</math>.


=== Feynman-Kac formula===
Let's derive the Feyman Kac formula for  <math>Z(x,t)</math> in the general case:
* First, focus on free paths and introduce the following probability
<center> <math>
P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 \right] \delta\left(  \int_0^t d \tau V(x(\tau),\tau)-A \right)
</math></center>
* Second, the moments generating function
<center> <math>
Z_p(x ,t) = \int_{-\infty}^\infty d A e^{-p A} P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) e^{-\frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2  -p \int_0^t d \tau  V(x(\tau),\tau)}
</math></center>
* Third, consider free paths evolving up to <math>t+dt</math> and reaching <math>x</math> :
<center> <math>
Z_p(x,t+dt)= \left\langle e^{-p \int_0^{t+dt} d \tau  V(x(\tau),\tau)}\right\rangle=  \left\langle e^{-p \int_0^{t} d \tau  V(x(\tau),\tau)}\right\rangle e^{-p V(x,t) d t } =[1 -p V(x,t) d t +\dots]\left\langle Z_p(x-\Delta x,t) \right\rangle_{\Delta x}
</math></center>
Here  <math>  \langle \ldots \rangle</math> is the average over all free paths, while  <math>  \langle \ldots \rangle_{\Delta x}</math> is the average over the last jump, namely  <math>  \langle \Delta x \rangle=0
</math> and  <math>  \langle \Delta x^2 \rangle=T d t  </math>.
* At the lowest order we have
* At the lowest order we have
<center> <math>
<math display="block">
Z_p(x,t+dt)= Z_p(x,t) +dt \left[ \frac{T}{2} \partial_x^2 Z_p -p V(x,t) Z_p \right] +O(dt^2)
Z_p(x,t+dt)
</math></center>
= Z_p(x,t) + dt\left[\frac{T}{2}\partial_x^2 Z_p - pV(x,t)Z_p\right] + O(dt^2).
Replacing <math> p=1/T</math> we obtain the partition function is the solution of the Schrodinger-like equation:
<center> <math>
\partial_t Z(x,t) =-  \hat H Z = - \left[ -\frac{T}{2}\frac{d^2 }{d x^2} + \frac{V(x,\tau)}{T}\right] Z(x,t)
</math>
</math>
<math>
 
Z[x,t=0]=\delta(x)
Replacing <math>p=1/T</math> we obtain that the partition function is the solution of the Schrödinger-like equation:
<math display="block">
\partial_t Z(x,t)
= -\hat H Z
= -\left[-\frac{T}{2}\frac{d^2}{dx^2} + \frac{V(x,t)}{T}\right] Z(x,t),
\qquad
Z(x,t=0)=\delta(x).
</math>
</math>
</center>


===Remarks===
=== Remarks ===
 
<Strong>Remark 1:</Strong>
<Strong>Remark 1:</Strong>


This equation is a diffusive equation with multiplicative noise <math>V(x,\tau)/T</math> . Edwards Wilkinson is instead a diffusive equation with additive noise.  
This equation is a diffusive equation with multiplicative noise <math>V(x,t)/T</math>. Edwards–Wilkinson is instead a diffusive equation with additive noise.


<Strong>Remark 2:</Strong>
<Strong>Remark 2:</Strong>
This hamiltonian is time dependent because of the multiplicative noise <math>V(x,\tau)/T</math>. For a <Strong> time independent </Strong> hamiltonian, we can use the spectrum of the operator. In general we will have to parts:
* A discrete set of eigenvalues <math>E_n</math> with the eigenstates <math>\psi_n(x)</math>
* A continuum part where the states <math>\psi_E(x)</math> have energy <math>E</math>. We define the density of states  <math>\rho(E)</math>, such that the number of states with energy in (<math>E, E + dE</math>) is <math>\rho(E) dE </math>.


In this case <math> Z[x,t] </math> can be written has the sum of two contributions:
This Hamiltonian is time dependent because of the multiplicative noise <math>V(x,t)/T</math>. For a <Strong>time independent</Strong> Hamiltonian, we can use the spectrum of the operator. In general we will have two parts:
<center> <math>
 
Z[x,t] = \left( e^{- \hat H t} \right)_{0 \to x}= \sum_n \psi_n(0) \psi_n^*(x) e^{- E_n t} + \int_0^\infty dE \, \rho(E) \psi_E(0) \psi_E^*(x) e^{- E t}.
* A discrete set of eigenvalues <math>E_n</math> with eigenstates <math>\psi_n(x)</math>
* A continuum part where the states <math>\psi_E(x)</math> have energy <math>E</math>. We define the density of states <math>\rho(E)</math>, such that the number of states with energy in <math>(E,E+dE)</math> is <math>\rho(E)\,dE</math>.
 
In this case <math>Z(x,t)</math> can be written as the sum of two contributions:
<math display="block">
Z(x,t)
= \left(e^{-\hat H t}\right)_{0\to x}
= \sum_n \psi_n(0)\psi_n^*(x)e^{-E_n t}
+ \int_0^\infty dE\,\rho(E)\,\psi_E(0)\psi_E^*(x)e^{-Et}.
</math>
</math>
</center>
 
In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes:
In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes:
<center> <math>
<math display="block">
Z_{\text{free}}(x,t)=\frac{e^{-x^2/(2Tt)}}{\sqrt{2 \pi Tt}}
Z_{\text{free}}(x,t)=\frac{e^{-x^2/(2Tt)}}{\sqrt{2\pi Tt}}.
</math></center>
</math>


== Feynman-Kac formula==
==== Hints: free particle in 1D ====
Let's derive the Feyman Kac formula for  <math>Z(x,t)</math> in the general case:
 
* First, focus on free paths and introduce the following probability
For a free particle in one dimension the Hamiltonian is <math>\hat H = -\frac{T}{2}\,\partial_x^2</math>.
<center> <math>
 
P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 \right] \delta\left(  \int_0^t d \tau V(x(\tau),\tau)-A \right)
'''Spectrum.'''
</math></center>
The spectrum is purely continuous. The eigenstates are plane waves
* Second, the moments generating function
<math display="block">
<center> <math>
\psi_k(x)=\frac{1}{\sqrt{2\pi}}e^{ikx},
Z_p(x ,t) = \int_{-\infty}^\infty d A e^{-p A} P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) e^{-\frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2  -p \int_0^t d \tau  V(x(\tau),\tau)}
\qquad
</math></center>
E_k=\frac{T k^2}{2},
* Third, the backward approach. Consider free paths evolving up to <math>t+dt</math> and reaching <math>x</math> :
<center> <math>
Z_p(x,t+dt)= \left\langle e^{-p \int_0^{t+dt} d \tau  V(x(\tau),\tau)}\right\rangle=  \left\langle e^{-p \int_0^{t} d \tau  V(x(\tau),\tau)}\right\rangle e^{-p V(x,t) d t } =[1 -p V(x,t) d t +\dots]\left\langle Z_p(x-\Delta x,t) \right\rangle_{\Delta x}
</math></center>
Here  <math>  \langle \ldots \rangle</math> is the average over all free paths, while  <math>  \langle \ldots \rangle_{\Delta x}</math> is the average over the last jump, namely  <math>  \langle \Delta x \rangle=0
</math> and  <math>  \langle \Delta x^2 \rangle=T d t  </math>.
* At the lowest order we have
<center> <math>
Z_p(x,t+dt)= Z_p(x,t) +dt \left[ \frac{T}{2} \partial_x^2 Z_p -p V(x,t) Z_p \right] +O(dt^2)
</math></center>
Replacing <math> p=1/T</math> we obtain the partition function is the solution of the Schrodinger-like equation:
<center> <math>
\partial_t Z(x,t) =-  \hat H Z = - \left[ -\frac{T}{2}\frac{d^2 }{d x^2} + \frac{V(x,\tau)}{T}\right] Z(x,t)
</math>
</math>
<math>
with <math>k\in\mathbb{R}</math>. The states are delocalized and satisfy Dirac delta normalization
Z[x,t=0]=\delta(x)
<math display="block">
\int_{-\infty}^{\infty} dx\,\psi_{k'}^*(x)\psi_k(x)=\delta(k-k').
</math>
</math>
</center>


===Remarks===
'''Energy representation and density of states.'''
<Strong>Remark 1:</Strong>
For a given energy <math>E>0</math> there are two degenerate states,
<math display="block">
\psi_E^{\pm}(x)=\frac{1}{\sqrt{2\pi}}\,e^{\pm i\sqrt{2E/T}\,x}.
</math>
The density of states is obtained from
<math display="block">
\rho(E)=\int_{-\infty}^{\infty} dk\,\delta(E-E_k),
\qquad
E_k=\frac{T k^2}{2}.
</math>


This equation is a diffusive equation with multiplicative noise. Edwards Wilkinson is instead a diffusive equation with additive noise. The Cole Hopf transformation allows to map the diffusive equation with multiplicative noise in a non-linear equation with additive noise. We will apply this tranformation and have a surprise.
'''Propagator.'''
Using the spectral decomposition one can write
<math display="block">
Z(x,t)
=\int_0^{\infty} dE\,\rho(E)
\sum_{\sigma=\pm}
\psi_E^{\sigma}(0)\psi_E^{\sigma *}(x)\,e^{-Et}.
</math>
Evaluating the resulting Gaussian integral yields
<math display="block">
Z_{\text{free}}(x,t)=\frac{e^{-x^2/(2Tt)}}{\sqrt{2\pi Tt}}.
</math>


<Strong>Remark 2:</Strong>
Useful identity:
This hamiltonian is time dependent because of the multiplicative noise <math>V(x,\tau)/T</math>. For a <Strong> time independent </Strong> hamiltonian, we can use the spectrum of the operator. In general we will have to parts:
<math display="block">
* A discrete set of eigenvalues <math>E_n</math> with the eigenstates <math>\psi_n(x)</math>
\int_{-\infty}^{\infty} dx\,e^{-(a x^2+b x)}
* A continuum part where the states <math>\psi_E(x)</math> have energy <math>E</math>. We define the density of states (DOS) <math>\rho(E)</math>, such that the number of states with energy in <math>(E, E+dE)</math> is <math>\rho(E) dE </math>.
=\sqrt{\frac{\pi}{a}}\,e^{\,b^2/(4a)},\qquad a>0.
In this case <math> Z[x,t] </math> can be written has the sum of two contributions:
<center> <math>
Z[x,t] = \left( e^{- \hat H t} \right)_{0 \to x}= \sum_n \psi_n(0) \psi_n^*(x) e^{- E_n t} + \int_0^\infty dE \, \rho(E) \psi_E(0) \psi_E^*(x) e^{- E t}.
</math>
</math>
</center>


====Exercise: free particle in 1D====
== Cole Hopf Transformation ==
* <Strong> Step 1: The spectrum</Strong>
For a free particle there a no discrete eigenvalue, but only a continuum spectrum. In 1D the hamiltonian is <math> \hat H =-(T/2) \partial_x^2</math>.
Show that the continuum spectrum  has the form
<center><math> \psi_k(x) =\frac{1}{\sqrt{2 \pi}} e^{i k x} ,\quad \text{with}  \; E_k =\frac{ T k^2}{2}  </math></center>
Here <math>k </math> is a real number, while <math>E_k</math> is a positive number. Note that the states are delocalized on the entire real axis and they cannot be normalized to unit but you impose the Dirac delta normalization:
<center><math> \int_{-\infty}^\infty \, dx \, \psi_{k'}^*(x)\psi_k(x) = \delta(k-k')  </math></center>
* <Strong> Step 2: the DOS </Strong>
For a given energy <math> E  </math> there are two states
<center><math> \psi_E^{-}(x) =\frac{1}{\sqrt{2 \pi}} e^{-i \sqrt{2 E/T} x} ,\quad \text{and}  ,\quad \psi_E^{+}(x) =\frac{1}{\sqrt{2 \pi}} e^{i \sqrt{2 E/T} x}  </math></center>
Use the definition of DOS <center><math> \rho(E)=  \int_{-\infty}^\infty \, dk \,\delta(E-E_k) </math></center> ans show that for both <math> \psi_E^{-}(x) , \psi_E^{-}(x)  </math> you have  :
<center><math> \rho(E) = \frac{1}{\sqrt{2 E T} }  </math></center>
* <Strong> Step 3: the propagator </Strong>
Use the spectral decomposition of the propagator to recover <math> Z_{\text{free}}(x,t)</math>. <Strong>
Tip:</Strong> use <math> \int_{-\infty}^\infty \, dx \, \exp[-(a x^2 +b x)]= \sqrt{\pi/a} \exp[b^2/(4 a)]  </math>.


== Cole Hopf Transformation==
Replacing
Replacing  
* <math>T = 2\nu</math>
* <math>T =2 \nu </math>
* <math>x = r</math>
* <math>x = r </math>
* <math>Z(x,t) = \exp\!\left(\frac{\lambda}{2\nu}h(r,t)\right)</math>
* <math>   Z(x,t) = \exp\left(\frac{\lambda}{2 \nu} h(r,t) \right) </math>
* <math>-V(x,t)=\lambda\,\eta(r,t)</math>
* <math>- V(x,t)=\lambda \eta(r,t) </math>
 
You get
you get
<center> <math>
<math display="block">
\partial_t h(r,t)= \nu \nabla^2 h(r,t)+ \frac{\lambda}{2} (\nabla h)^2 + \eta(r,t)
\partial_t h(r,t)= \nu \nabla^2 h(r,t)+ \frac{\lambda}{2}(\nabla h)^2 + \eta(r,t).
</math></center>
</math>
The KPZ equation!  
The KPZ equation!


We can establish a KPZ/Directed polymer dictionary, valid in any dimension. Let us remark that the free energy of the polymer is
We can establish a KPZ/Directed polymer dictionary, valid in any dimension. Let us remark that the free energy of the polymer is
<center> <math>
<math display="block">
F= - T \ln Z(x,t) = \frac{-1}{\lambda} h(r,t)
F = -T\ln Z(x,t) = -\frac{1}{\lambda}h(r,t).
</math></center>
</math>
At low temperature, the free energy approaches the ground state energy, <math>E_{\min}</math>.
At low temperature, the free energy approaches the ground state energy <math>E_{\min}</math>.


{| class="wikitable"
{| class="wikitable"
|+ Dictionary
|+ KPZ / Directed Polymer dictionary
|-
|-
! KPZ !! KPZ exponents !! Directed polymer !! Directed polymer exponents
! KPZ quantity !! KPZ scaling !! Directed polymer quantity !! Directed polymer scaling
|-
|-
| <math> r </math>|| <math> r \sim t^{1/z}</math>|| <math>x </math>|| <math>x\sim t^{\zeta}</math>
| <math>r</math>
| <math>r \sim t^{1/z}</math>
| <math>x</math>
| <math>x \sim t^{\zeta}</math>
|-
|-
| <math>t</math> ||<math>h(r,t) \sim t^{\alpha/z}</math> || <math>t</math>||
| <math>t</math>
| <math>h(r,t) \sim t^{\alpha/z}</math>
| <math>t</math>
| <math>\overline{(E_{\min}-\overline{E_{\min}})^2} \sim t^{2\theta}</math>
|-
|-
| <math>h</math> || <math>h(r,t) \sim r^{\alpha}</math>|| <math>F, E_{\min}</math> || <math> \overline{(E_{\min} - \overline{E_{\min}})^2} \sim t^{2\theta} </math>
| <math>h</math>
| <math>h(r,t) \sim r^{\alpha}</math>
| <math>F,\,E_{\min}</math>
| <math>\overline{(E_{\min}-\overline{E_{\min}})^2} \sim t^{2\theta}</math>
|}
|}


We conclude that  
We conclude that
<center> <math>
<math display="block">
\theta =\alpha/z, \quad \zeta=1/z  
\theta = \alpha/z,
</math></center>
\quad
Moreover, the scaling relation <math>
\zeta = 1/z.
\theta =2 \zeta- 1  
</math>
</math> is a reincarnation of the Galilean invariance <math>
Moreover, the scaling relation <math>\theta = 2\zeta - 1</math> is a reincarnation of the Galilean invariance <math>\alpha + z = 2</math>.
\alpha +z =2
</math>.

Latest revision as of 22:02, 1 March 2026

Goal: This lecture is dedicated to a classical model in disordered systems: the directed polymer in random media. It has been introduced to model vortices in superconductors or domain walls in magnetic films. We will focus on algorithms that identify the ground state or compute the free energy at temperature T, as well as on the Cole–Hopf transformation that maps this model to the KPZ equation.

Directed Polymers (d = 1)

The configuration is described by a vector function x(t), where t is the internal coordinate. The polymer lives in D=1+N dimensions.

Examples: vortex lines, DNA strands, fronts.

Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.

Directed Polymers on a lattice

Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left or one step right. A random energy V(τ,x) is associated to each node and the total energy is simply E[x(τ)]=τ=0tV(τ,x).

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 one to identify the minimal energy among the exponential number of configurations x(τ): Emin=minx(τ)E[x(τ)].

We are also interested in the ground state configuration xmin(τ). For both quantities we expect scale invariance with two exponents θ, ζ for the energy and for the roughness: Emin=ct+κ1tθχ,xmin(t/2)κ2tζχ~.

Universal exponents: Both θ and ζ are independent of the lattice, the disorder distribution, the elastic constants, or the boundary conditions.

Non-universal constants: c, κ1, κ2 are of order 1 and depend on the lattice, the disorder distribution, the elastic constants, etc. However c is independent of the boundary conditions.

Universal distributions: χ, χ~ are universal, but depend on the boundary conditions. Starting from 2000, a remarkable connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular, we discuss two different boundary conditions:

  • Droplet: x(τ=0)=x(τ=t)=0. In this case, up to rescaling, χ is distributed as the smallest eigenvalue of a GUE random matrix (Tracy–Widom distribution F2(χ)).
  • Flat: x(τ=0)=0 while the other end x(τ=t) is free. In this case, up to rescaling, χ is distributed as the smallest eigenvalue of a GOE random matrix (Tracy–Widom distribution F1(χ)).

Entropy and scaling relation

It is useful to compute the entropy Entropy=ln(ttx2)tln2x22t+O(x4). From which one could guess from dimensional analysis θ=2ζ1. This relation is actually exact also for the continuum model.

Directed polymers in the continuum

We now reanalyze the previous problem in the presence of quenched disorder. Instead of discussing the case of interfaces, we will focus on directed polymers.

Let us consider polymers x(τ) of length t. The energy associated with a given polymer configuration can be written as E[x(τ)]=0tdτ[12(dxdτ)2+V(x(τ),τ)]. The first term describes the elastic energy of the polymer, while the second one is the disordered potential, which we assume to be V(x,τ)=0,V(x,τ)V(x,τ)=Dδ(xx)δ(ττ). where D is the disorder strength.

Polymer partition function and propagator of a quantum particle

Let us consider polymers starting at 0, ending at x and at thermal equilibrium at temperature T. The partition function of the model reads Z(x,t)=x(0)=0x(t)=x𝒟x(τ)exp[1T0tdτ(12(τx)2+V(x(τ),τ))]. Here, the partition function is written as a sum over all possible paths, corresponding to all polymer configurations that start at 0 and end at x, weighted by the appropriate Boltzmann factor.

Let's perform the following change of variables: τ=it. We also identify T with and t~=it as the time. Z(x,t~)=x(0)=0x(t~)=x𝒟x(t)exp[i0t~dt(12(tx)2V(x(t),t))].

Note that S[x]=0t~dt(12(tx)2V(x(t),t)) is the classical action of a particle with kinetic energy 12(tx)2 and time-dependent potential V(x(t),t), evolving from time zero to time t~. From the Feynman path integral formulation, Z(x,t~) is the propagator of the quantum particle.

Feynman–Kac formula

Let's derive the Feynman–Kac formula for Z(x,t) in the general case:

  • First, focus on free paths and introduce the following probability

P[A,x,t]=x(0)=0x(t)=x𝒟x(τ)exp[1T0tdτ12(τx)2]δ(0tdτV(x(τ),τ)A).

  • Second, the moment generating function

Zp(x,t)=dAepAP[A,x,t]=x(0)=0x(t)=x𝒟x(τ)exp[1T0tdτ12(τx)2p0tdτV(x(τ),τ)].

  • Third, consider free paths evolving up to t+dt and reaching x:

Zp(x,t+dt)=ep0t+dtdτV(x(τ),τ)=ep0tdτV(x(τ),τ)epV(x,t)dt=[1pV(x,t)dt+]Zp(xΔx,t)Δx. Here is the average over all free paths, while Δx is the average over the last jump, namely Δx=0 and Δx2=Tdt.

  • At the lowest order we have

Zp(x,t+dt)=Zp(x,t)+dt[T2x2ZppV(x,t)Zp]+O(dt2).

Replacing p=1/T we obtain that the partition function is the solution of the Schrödinger-like equation: tZ(x,t)=H^Z=[T2d2dx2+V(x,t)T]Z(x,t),Z(x,t=0)=δ(x).

Remarks

Remark 1:

This equation is a diffusive equation with multiplicative noise V(x,t)/T. Edwards–Wilkinson is instead a diffusive equation with additive noise.

Remark 2:

This Hamiltonian is time dependent because of the multiplicative noise V(x,t)/T. For a time independent Hamiltonian, we can use the spectrum of the operator. In general we will have two parts:

  • A discrete set of eigenvalues En with eigenstates ψn(x)
  • A continuum part where the states ψE(x) have energy E. We define the density of states ρ(E), such that the number of states with energy in (E,E+dE) is ρ(E)dE.

In this case Z(x,t) can be written as the sum of two contributions: Z(x,t)=(eH^t)0x=nψn(0)ψn*(x)eEnt+0dEρ(E)ψE(0)ψE*(x)eEt.

In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes: Zfree(x,t)=ex2/(2Tt)2πTt.

Hints: free particle in 1D

For a free particle in one dimension the Hamiltonian is H^=T2x2.

Spectrum. The spectrum is purely continuous. The eigenstates are plane waves ψk(x)=12πeikx,Ek=Tk22, with k. The states are delocalized and satisfy Dirac delta normalization dxψk*(x)ψk(x)=δ(kk).

Energy representation and density of states. For a given energy E>0 there are two degenerate states, ψE±(x)=12πe±i2E/Tx. The density of states is obtained from ρ(E)=dkδ(EEk),Ek=Tk22.

Propagator. Using the spectral decomposition one can write Z(x,t)=0dEρ(E)σ=±ψEσ(0)ψEσ*(x)eEt. Evaluating the resulting Gaussian integral yields Zfree(x,t)=ex2/(2Tt)2πTt.

Useful identity: dxe(ax2+bx)=πaeb2/(4a),a>0.

Cole Hopf Transformation

Replacing

  • T=2ν
  • x=r
  • Z(x,t)=exp(λ2νh(r,t))
  • V(x,t)=λη(r,t)

you get th(r,t)=ν2h(r,t)+λ2(h)2+η(r,t). The KPZ equation!

We can establish a KPZ/Directed polymer dictionary, valid in any dimension. Let us remark that the free energy of the polymer is F=TlnZ(x,t)=1λh(r,t). At low temperature, the free energy approaches the ground state energy Emin.

KPZ / Directed Polymer dictionary
KPZ quantity KPZ scaling Directed polymer quantity Directed polymer scaling
r rt1/z x xtζ
t h(r,t)tα/z t (EminEmin)2t2θ
h h(r,t)rα F,Emin (EminEmin)2t2θ

We conclude that θ=α/z,ζ=1/z. Moreover, the scaling relation θ=2ζ1 is a reincarnation of the Galilean invariance α+z=2.