Difference between revisions of "Properties of the Linear Schrodinger Equation"

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+u_0 w\left(  0\right)  =0.</math> This final condition is obtained by integrating `across' zero as follows
 
+u_0 w\left(  0\right)  =0.</math> This final condition is obtained by integrating `across' zero as follows
 
<center><math>\begin{align}
 
<center><math>\begin{align}
\int_{0^{-}}^{0^{+}} \partial_x^2 w +\delta(x) w + \lambda w \ dx = 0.
+
\int_{0^{-}}^{0^{+}} \left(\partial_x^2 w +u_0\delta(x) w + \lambda w \right) \ \mathrm{d}x = 0.
 
\end{align}
 
\end{align}
 
</math></center>
 
</math></center>
Line 54: Line 54:
 
<math>k=u_{0}/2.</math> We need to normalise the eigenfunctions so that
 
<math>k=u_{0}/2.</math> We need to normalise the eigenfunctions so that
 
<center><math>
 
<center><math>
\int_{-\infty}^{\infty}\left(  w\left(  x\right)  \right)  ^{2}dx=1.
+
\int_{-\infty}^{\infty}\left(  w\left(  x\right)  \right)  ^{2}\mathrm{d}x=1.
 
</math></center>
 
</math></center>
 
Therefore
 
Therefore
 
<center><math>
 
<center><math>
2\int_{0}^{\infty}\left(  ae^{-u_{0}x/2}\right)  ^{2}dx=1
+
2\int_{0}^{\infty}\left(  ae^{-u_{0}x/2}\right)  ^{2}\mathrm{d}x=1
 
</math></center>
 
</math></center>
 
which means that <math>a=\sqrt{u_{0}/2}.</math> Therefore, there is only one discrete
 
which means that <math>a=\sqrt{u_{0}/2}.</math> Therefore, there is only one discrete
Line 77: Line 77:
  
 
\mathrm{e}^{-\mathrm{i}kx}+r\mathrm{e}^{\mathrm{i}kx}, & x<0\\
 
\mathrm{e}^{-\mathrm{i}kx}+r\mathrm{e}^{\mathrm{i}kx}, & x<0\\
a\mathrm{e}^{-\mathrm{i}kx}, & x>0
+
t\mathrm{e}^{-\mathrm{i}kx}, & x>0
 
\end{matrix}
 
\end{matrix}
 
\right.
 
\right.
Line 85: Line 85:
 
+u_{0}w\left(  0\right)  =0.</math> This gives us
 
+u_{0}w\left(  0\right)  =0.</math> This gives us
 
<center><math>\begin{matrix}
 
<center><math>\begin{matrix}
1+r  &  =a\\
+
1+r  &  =t\\
-ika+ik-ikr  &  =-au_{0}
+
-ikt+ik-ikr  &  =-tu_{0}
 
\end{matrix}</math></center>
 
\end{matrix}</math></center>
 
which has solution
 
which has solution
 
<center><math>\begin{matrix}
 
<center><math>\begin{matrix}
 
r  &  =\frac{u_{0}}{2ik-u_{0}}\\
 
r  &  =\frac{u_{0}}{2ik-u_{0}}\\
a &  =\frac{2ik}{2ik-u_{0}}
+
t &  =\frac{2ik}{2ik-u_{0}}
 
\end{matrix}</math></center>
 
\end{matrix}</math></center>
  
Line 109: Line 109:
 
where <math>b>0.</math>
 
where <math>b>0.</math>
  
===Case when <math>\lambda>0</math>===
+
===Case when <math>\lambda<0</math>===
  
 
If we solve this equation for the case when <math>\lambda<0,</math> <math>\lambda=-k^{2}</math> we
 
If we solve this equation for the case when <math>\lambda<0,</math> <math>\lambda=-k^{2}</math> we
Line 116: Line 116:
 
w\left(  x\right)  =\left\{
 
w\left(  x\right)  =\left\{
 
\begin{matrix}
 
\begin{matrix}
 
+
a_{1}e^{kx}, & x<-\varsigma,\\
a_{1}e^{kx}, & x<-\varsigma\\
+
b_{1}\cos\kappa x+b_{2}\sin\kappa x, & -\varsigma< x <\varsigma,\\
b_{1}\cos\kappa x+b_{2}\sin\kappa x & -\varsigma<x<\varsigma\\
+
a_{2}e^{-kx}, & x>\varsigma,
a_{2}e^{-kx} & x>\varsigma
 
 
\end{matrix}
 
\end{matrix}
 
\right.
 
\right.
Line 126: Line 125:
 
no solution for <math>k>\sqrt{b}).</math> We then match <math>w</math> and its derivative at
 
no solution for <math>k>\sqrt{b}).</math> We then match <math>w</math> and its derivative at
 
<math>x=\pm\varsigma</math> to solve for <math>a</math> and <math>b</math>. This leads to two system of
 
<math>x=\pm\varsigma</math> to solve for <math>a</math> and <math>b</math>. This leads to two system of
equation, one for the even (<math>a_{1}=a_{2}</math> and <math>b_{2}=0</math> ) and one for the odd
+
equations, one for the even (<math>a_{1}=a_{2}</math> and <math>b_{2}=0</math> ) and one for the odd
 
solutions (<math>a_{1}=-a_{2}</math> and <math>b_{1}=0)</math>. The solution for the even solutions
 
solutions (<math>a_{1}=-a_{2}</math> and <math>b_{1}=0)</math>. The solution for the even solutions
 
is
 
is
 +
<center><math>
 +
w\left(  x\right)  =\left\{
 +
\begin{matrix}
 +
a_{1}e^{kx}, & x<-\varsigma,\\
 +
b_{1}\cos\kappa x, & -\varsigma< x <\varsigma,\\
 +
a_{1}e^{-kx}, & x>\varsigma,
 +
\end{matrix}
 +
\right.
 +
</math></center>
 +
If we impose the condition that the function and its derivative are continuous at
 +
<math>x=\pm\varsigma</math> we obtain the following equation
 
<center><math>
 
<center><math>
 
\left(
 
\left(
Line 173: Line 183:
  
 
The solution for the odd solutions is
 
The solution for the odd solutions is
 +
<center><math>
 +
w\left(  x\right)  =\left\{
 +
\begin{matrix}
 +
a_{1}e^{kx}, & x <-\varsigma,\\
 +
b_{2}\sin\kappa x, & -\varsigma< x <\varsigma,\\
 +
-a_{1}e^{-kx} & x > \varsigma,
 +
\end{matrix}
 +
\right.
 +
</math></center>
 +
and again imposing the condition that the solution and its derivative is continuous
 +
at <math>x=\pm\varsigma</math> gives
 
<center><math>
 
<center><math>
 
\left(
 
\left(
 
\begin{matrix}
 
\begin{matrix}
  
e^{-k\varsigma} & -\sin\kappa\varsigma\\
+
e^{-k\varsigma} & \sin\kappa\varsigma\\
ke^{-k\varsigma} & \cos\kappa\varsigma
+
ke^{-k\varsigma} & -\kappa\cos\kappa\varsigma
 
\end{matrix}
 
\end{matrix}
 
\right)  \left(
 
\right)  \left(
Line 199: Line 220:
 
\begin{matrix}
 
\begin{matrix}
  
e^{-k\varsigma} & -\sin\kappa\varsigma\\
+
e^{-k\varsigma} & \sin\kappa\varsigma\\
ke^{-k\varsigma} & \kappa\cos\kappa\varsigma
+
ke^{-k\varsigma} & -\kappa\cos\kappa\varsigma
 
\end{matrix}
 
\end{matrix}
 
\right)  =0
 
\right)  =0
Line 220: Line 241:
 
\begin{matrix}
 
\begin{matrix}
  
\mathrm{e}^{-\mathrm{i}kx}+r\mathrm{e}^{\mathrm{i}kx}, & x<-\varsigma\\
+
\mathrm{e}^{-\mathrm{i}kx}+r\mathrm{e}^{\mathrm{i}kx}, & x <-\varsigma\\
b_{1}\cos\kappa x+b_{2}\sin\kappa x & -\varsigma<x<\varsigma\\
+
b_{1}\cos\kappa x+b_{2}\sin\kappa x & -\varsigma< x <\varsigma\\
a\mathrm{e}^{-\mathrm{i}kx} & x>\varsigma
+
t\mathrm{e}^{-\mathrm{i}kx} & x>\varsigma
 
\end{matrix}
 
\end{matrix}
 
\right.
 
\right.
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b_{1}\\
 
b_{1}\\
 
b_{2}\\
 
b_{2}\\
a
+
t
 
\end{matrix}
 
\end{matrix}
 
\right)  =\left(
 
\right)  =\left(
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\right)
 
\right)
 
</math></center>
 
</math></center>
 +
 +
== Lecture Videos ==
 +
 +
=== Part 1 ===
 +
 +
{{#ev:youtube|anAThvCcpNw}}
 +
 +
=== Part 2 ===
 +
 +
{{#ev:youtube|SDPIx42VjLQ}}
 +
 +
=== Part 3 ===
 +
 +
{{#ev:youtube|OUmjeLZWr3M}}
 +
 +
=== Part 4 ===
 +
 +
{{#ev:youtube|hIfcO3a8_XU}}
 +
 +
=== Part 5 ===
 +
 +
{{#ev:youtube|z13lKSTficA}}
 +
 +
=== Part 6 ===
 +
 +
{{#ev:youtube|2XlQpEscxE4}}
 +
 +
=== Part 7 ===
 +
 +
{{#ev:youtube|iMMQ4NUdXNc}}
 +
 +
=== Part 8 ===
 +
 +
{{#ev:youtube|0F_dINNxMlw}}

Latest revision as of 01:03, 24 September 2020

Nonlinear PDE's Course
Current Topic Properties of the Linear Schrodinger Equation
Next Topic Connection betwen KdV and the Schrodinger Equation
Previous Topic Introduction to the Inverse Scattering Transform


The linear Schrodinger equation

[math]\displaystyle{ \partial_{x}^{2}w+uw=-\lambda w }[/math]

has two kinds of solutions for [math]\displaystyle{ u\rightarrow0 }[/math] as [math]\displaystyle{ x\rightarrow\pm\infty. }[/math] The first are waves and the second are bound solutions. It is well known that there are at most a finite number of bound solutions (provided [math]\displaystyle{ u\rightarrow0 }[/math] as [math]\displaystyle{ x\pm\infty }[/math] sufficiently rapidly) and a continum of solutions for the incident waves. This is easiest seen through the following examples

Example 1: [math]\displaystyle{ \delta }[/math] function potential

We consider here the case when [math]\displaystyle{ u\left( x,0\right) = u_0 \delta\left( x\right) . }[/math] Note that this function can be thought of as the limit as of the potential

[math]\displaystyle{ u\left( x\right) =\left\{ \begin{matrix} 0 & x\notin\left[ -\varepsilon,\varepsilon\right] \\ \frac{u_{0}}{2\varepsilon} & x\in\left[ -\varepsilon,\varepsilon\right] \end{matrix} \right. }[/math]

In this case we need to solve

[math]\displaystyle{ \partial_{x}^{2}w+ u_0\delta(x) w=-\lambda w }[/math]

We consider the case of [math]\displaystyle{ \lambda\lt 0 }[/math] and [math]\displaystyle{ \lambda\gt 0 }[/math] separately. For the first case we write [math]\displaystyle{ \lambda=-k^{2} }[/math] and we obtain

[math]\displaystyle{ w\left( x\right) =\left\{ \begin{matrix} ae^{kx}, & x\lt 0\\ be^{-kx}, & x\gt 0 \end{matrix} \right. }[/math]

We have two conditions at [math]\displaystyle{ x=0, }[/math] [math]\displaystyle{ w }[/math] must be continuous at [math]\displaystyle{ 0 }[/math] and [math]\displaystyle{ \partial_{x}w\left( 0^{+}\right) -\partial_{x}w\left( 0^{-}\right) +u_0 w\left( 0\right) =0. }[/math] This final condition is obtained by integrating `across' zero as follows

[math]\displaystyle{ \begin{align} \int_{0^{-}}^{0^{+}} \left(\partial_x^2 w +u_0\delta(x) w + \lambda w \right) \ \mathrm{d}x = 0. \end{align} }[/math]

This gives the condition that [math]\displaystyle{ a=b }[/math] and [math]\displaystyle{ k=u_{0}/2. }[/math] We need to normalise the eigenfunctions so that

[math]\displaystyle{ \int_{-\infty}^{\infty}\left( w\left( x\right) \right) ^{2}\mathrm{d}x=1. }[/math]

Therefore

[math]\displaystyle{ 2\int_{0}^{\infty}\left( ae^{-u_{0}x/2}\right) ^{2}\mathrm{d}x=1 }[/math]

which means that [math]\displaystyle{ a=\sqrt{u_{0}/2}. }[/math] Therefore, there is only one discrete spectral point which we denote by [math]\displaystyle{ k_{1}=u_{0}/2 }[/math]

[math]\displaystyle{ w_{1}\left( x\right) =\left\{ \begin{matrix} \sqrt{k_{1}}e^{k_{1}x}, & x\lt 0\\ \sqrt{k_{1}}e^{-k_{1}x}, & x\gt 0 \end{matrix} \right. }[/math]

The continuous eigenfunctions correspond to [math]\displaystyle{ \lambda=k^{2}\gt 0 }[/math] are of the form

[math]\displaystyle{ w\left( x\right) =\left\{ \begin{matrix} \mathrm{e}^{-\mathrm{i}kx}+r\mathrm{e}^{\mathrm{i}kx}, & x\lt 0\\ t\mathrm{e}^{-\mathrm{i}kx}, & x\gt 0 \end{matrix} \right. }[/math]

Again we have the conditions that [math]\displaystyle{ w }[/math] must be continuous at [math]\displaystyle{ 0 }[/math] and [math]\displaystyle{ \partial_{x}w\left( 0^{+}\right) -\partial_{x}w\left( 0^{-}\right) +u_{0}w\left( 0\right) =0. }[/math] This gives us

[math]\displaystyle{ \begin{matrix} 1+r & =t\\ -ikt+ik-ikr & =-tu_{0} \end{matrix} }[/math]

which has solution

[math]\displaystyle{ \begin{matrix} r & =\frac{u_{0}}{2ik-u_{0}}\\ t & =\frac{2ik}{2ik-u_{0}} \end{matrix} }[/math]

Example 2: Hat Function Potential

The properties of the eigenfunction is perhaps seem most easily through the following example

[math]\displaystyle{ u\left( x\right) =\left\{ \begin{matrix} 0 & x\notin\left[ -\varsigma,\varsigma\right] \\ b & x\in\left[ -\varsigma,\varsigma\right] \end{matrix} \right. }[/math]

where [math]\displaystyle{ b\gt 0. }[/math]

Case when [math]\displaystyle{ \lambda\lt 0 }[/math]

If we solve this equation for the case when [math]\displaystyle{ \lambda\lt 0, }[/math] [math]\displaystyle{ \lambda=-k^{2} }[/math] we get

[math]\displaystyle{ w\left( x\right) =\left\{ \begin{matrix} a_{1}e^{kx}, & x\lt -\varsigma,\\ b_{1}\cos\kappa x+b_{2}\sin\kappa x, & -\varsigma\lt x \lt \varsigma,\\ a_{2}e^{-kx}, & x\gt \varsigma, \end{matrix} \right. }[/math]

where [math]\displaystyle{ \kappa=\sqrt{b-k^{2}} }[/math] which means that [math]\displaystyle{ 0\leq k\leq\sqrt{b} }[/math] (there is no solution for [math]\displaystyle{ k\gt \sqrt{b}). }[/math] We then match [math]\displaystyle{ w }[/math] and its derivative at [math]\displaystyle{ x=\pm\varsigma }[/math] to solve for [math]\displaystyle{ a }[/math] and [math]\displaystyle{ b }[/math]. This leads to two system of equations, one for the even ([math]\displaystyle{ a_{1}=a_{2} }[/math] and [math]\displaystyle{ b_{2}=0 }[/math] ) and one for the odd solutions ([math]\displaystyle{ a_{1}=-a_{2} }[/math] and [math]\displaystyle{ b_{1}=0) }[/math]. The solution for the even solutions is

[math]\displaystyle{ w\left( x\right) =\left\{ \begin{matrix} a_{1}e^{kx}, & x\lt -\varsigma,\\ b_{1}\cos\kappa x, & -\varsigma\lt x \lt \varsigma,\\ a_{1}e^{-kx}, & x\gt \varsigma, \end{matrix} \right. }[/math]

If we impose the condition that the function and its derivative are continuous at [math]\displaystyle{ x=\pm\varsigma }[/math] we obtain the following equation

[math]\displaystyle{ \left( \begin{matrix} e^{-k\varsigma} & -\cos\kappa\varsigma\\ ke^{-k\varsigma} & -\kappa\sin\kappa\varsigma \end{matrix} \right) \left( \begin{matrix} a_{1}\\ b_{1} \end{matrix} \right) =\left( \begin{matrix} 0\\ 0 \end{matrix} \right) }[/math]

This has non trivial solutions when

[math]\displaystyle{ \det\left( \begin{matrix} e^{-k\varsigma} & -\cos\kappa\varsigma\\ ke^{-k\varsigma} & -\kappa\sin\kappa\varsigma \end{matrix} \right) =0 }[/math]

which gives us the equation

[math]\displaystyle{ -\kappa e^{-k\varsigma}\sin\kappa\varsigma+k\cos\kappa\varsigma e^{-k\varsigma}=0 }[/math]

or

[math]\displaystyle{ \tan\kappa\varsigma=\frac{k}{\kappa} }[/math]

We know that [math]\displaystyle{ 0\lt \kappa\lt \sqrt{b} }[/math] and if we plot this we see that we obtain a finite number of solutions.

The solution for the odd solutions is

[math]\displaystyle{ w\left( x\right) =\left\{ \begin{matrix} a_{1}e^{kx}, & x \lt -\varsigma,\\ b_{2}\sin\kappa x, & -\varsigma\lt x \lt \varsigma,\\ -a_{1}e^{-kx} & x \gt \varsigma, \end{matrix} \right. }[/math]

and again imposing the condition that the solution and its derivative is continuous at [math]\displaystyle{ x=\pm\varsigma }[/math] gives

[math]\displaystyle{ \left( \begin{matrix} e^{-k\varsigma} & \sin\kappa\varsigma\\ ke^{-k\varsigma} & -\kappa\cos\kappa\varsigma \end{matrix} \right) \left( \begin{matrix} a_{1}\\ b_{1} \end{matrix} \right) =\left( \begin{matrix} 0\\ 0 \end{matrix} \right) }[/math]

This can non trivial solutions when

[math]\displaystyle{ \det\left( \begin{matrix} e^{-k\varsigma} & \sin\kappa\varsigma\\ ke^{-k\varsigma} & -\kappa\cos\kappa\varsigma \end{matrix} \right) =0 }[/math]

which gives us the equation

[math]\displaystyle{ \kappa e^{-k\varsigma}a\cos\kappa\varsigma+k\sin\kappa\varsigma e^{-k\varsigma}=0 }[/math]

or

[math]\displaystyle{ \tan\varsigma\kappa=-\frac{\kappa}{k} }[/math]

Case when [math]\displaystyle{ \lambda\gt 0 }[/math]

When [math]\displaystyle{ \lambda\gt 0 }[/math] we write [math]\displaystyle{ \lambda=k^{2} }[/math] and we obtain solution

[math]\displaystyle{ w\left( x\right) =\left\{ \begin{matrix} \mathrm{e}^{-\mathrm{i}kx}+r\mathrm{e}^{\mathrm{i}kx}, & x \lt -\varsigma\\ b_{1}\cos\kappa x+b_{2}\sin\kappa x & -\varsigma\lt x \lt \varsigma\\ t\mathrm{e}^{-\mathrm{i}kx} & x\gt \varsigma \end{matrix} \right. }[/math]

where [math]\displaystyle{ \kappa=\sqrt{b+k^{2}}. }[/math] Matching [math]\displaystyle{ w }[/math] and its derivaties at [math]\displaystyle{ x=\pm1 }[/math] we obtain

[math]\displaystyle{ \left( \begin{matrix} -\mathrm{e}^{-\mathrm{i}k\varsigma} & \cos\kappa\varsigma & -\sin\kappa\varsigma & 0\\ ik\mathrm{e}^{-\mathrm{i}k\varsigma} & \kappa\sin\kappa\varsigma & \kappa\cos\kappa \varsigma & 0\\ 0 & \cos\kappa\varsigma & \sin\kappa\varsigma & -\mathrm{e}^{-\mathrm{i}k\varsigma}\\ 0 & -\kappa\sin\kappa\varsigma & \kappa\cos\kappa\varsigma & ik\mathrm{e}^{-\mathrm{i}k\varsigma} \end{matrix} \right) \left( \begin{matrix} r\\ b_{1}\\ b_{2}\\ t \end{matrix} \right) =\left( \begin{matrix} \mathrm{e}^{\mathrm{i}k}\\ ik\mathrm{e}^{-\mathrm{i}k}\\ 0\\ 0 \end{matrix} \right) }[/math]

Lecture Videos

Part 1

Part 2

Part 3

Part 4

Part 5

Part 6

Part 7

Part 8