Solving PDF by using Fourier transform

In summary, the solution to the equation is $u(x,t)=\dfrac1{\sqrt{2\pi}}(F^{-1}(e^{-x^2})*e^{-\omega^2t}),$ where $F^{-1}(x)=-1$ and $e^{-x^2}=-\omega^2$.
  • #1
Markov2
149
0
$u_t-u_{xx}=0,$ $x\in\mathbb R,$ $t>0$ and $u(x,0)=e^{-x^2}.$

By applying Fourier transform on $t$ I have $\dfrac{\partial }{\partial t}F(u)+{{\omega }^{2}}F(u)=0,$ the solution of the latter equation is $F(u)(\omega,t)=ce^{-\omega^2t},$ now by applying the initial condition I have $F(u)(x,0)=c=e^{-x^2},$ so $F(u)(\omega,t)=e^{-x^2}e^{-\omega^2t}.$ So I use the convolution property to get $u(x,t)=\dfrac1{\sqrt{2\pi}}(e^{-x^2}*e^{-\omega^2t}).$

Is this correct?
Thanks!
 
Last edited:
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  • #2
インテグラルキラー;438 said:
$u_t-u_{xx}=0,$ $x\in\mathbb R,$ $t>0$ and $u(x,0)=e^{-x^2}.$

By applying Fourier transform on $t$ I have $\frac{\partial }{\partial t}F(u)+{{\omega }^{2}}F(u)=0,$ the solution of the latter equation is $F(u)(\omega,t)=ce^{-\omega^2t},$ now by applying the initial condition I have $F(u)(x,0)=c=e^{-x^2},$ so $F(u)(\omega,t)=e^{-x^2}e^{-\omega^2t}.$ So I use the convolution property to get $u(x,t)=\frac1{\sqrt{2\pi}}(e^{-x^2}*e^{-\omega^2t}).$

Is this correct?
Thanks!

No this is not correct.

You need to find the Fourier transform of $e^{-x^2}$ and that becomes your new initial conditions that you substitute.
 
  • #3
Okay, so the solution is actually $u(x,t)=\dfrac1{\sqrt{2\pi}}(F^{-1}(e^{-x^2})*e^{-\omega^2t}),$ or is still incorrect? How to do it then?
 
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  • #4
You should check that,
$$ \frac{1}{\sqrt{4\pi t}} \int \limits_{-\infty}^{\infty} \exp \left( - \frac{x^2}{4t} \right) e^{-i\omega x}~ dx = e^{-\omega^2 t} $$

Can you write down the solution now?
 
  • #5
Okay, I'm sorry, I'm a bit confused, on my post #3, does make sense the thing I wrote? I think it's not correct.
I suppose to use $F(f*g)(\xi)=\sqrt{2\pi}F(f )(\xi)\cdot F(g)(\xi).$

Thanks!
 

FAQ: Solving PDF by using Fourier transform

What is Fourier transform and how does it relate to solving PDF?

Fourier transform is a mathematical operation that breaks down a function into its constituent frequencies. It is used in solving PDF (Probability Density Function) because it allows us to analyze and manipulate complex functions, making it easier to solve for unknown variables.

Can Fourier transform be used to solve any type of PDF?

Yes, Fourier transform can be used to solve any type of PDF, as long as the function can be represented as a combination of simple trigonometric functions.

What are the advantages of using Fourier transform in PDF solving?

One advantage is that it allows us to solve for unknown variables in complex functions by breaking them down into simpler components. It also has applications in signal processing, image analysis, and many other fields.

Are there any limitations to using Fourier transform in PDF solving?

One limitation is that it assumes the function is periodic, meaning it repeats itself infinitely. This may not always be the case with real-world data. Additionally, rounding errors can occur when using numerical methods for Fourier transform, so it may not always be completely accurate.

Are there any alternative methods for solving PDF besides using Fourier transform?

Yes, there are other methods such as Monte Carlo simulation, numerical integration, and analytical methods like the maximum likelihood estimation. The choice of method depends on the complexity of the function and the available data.

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