Tricky Integral (fourier transforms)

  • Thread starter wolf party
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In summary, the conversation is about Fourier transforms and the specific integral INT[(1/(2*pi))*e^(ik(x-y)-(1+ik)ct) dk]. The questioner is unsure how to approach the integral and mentions that the delta function result has stumped them. They ask for any ideas and the responder clarifies that the constant e-ct is still present in the result. The conversation then discusses the integration of the delta function and concludes that the result is e^-ct*u(x-ct,0).
  • #1
wolf party
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the question is on Fourier transforms and i am stuck on how to approch the integral


INT[ (1/(2*pi))*e^(ik(x-y)-(1+ik)ct) dk


i know that the integral INT [(1/(2*pi))*e^(ik(x-y-ct)) dk] yields the delta function

= DELTA(x-y-ct) but the terms in the exponential from the integral in question have stumped me

any ideas would be great, cheers
 
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  • #2
What don't you like about the delta function result?

The e-ct is just a constant...
 
  • #3
would that mean we now have

(INT[e^-ct]) *DELTA(x-y-ct) ?
 
  • #4
wolf party said:
would that mean we now have

(INT[e^-ct]) *DELTA(x-y-ct) ?

No, the integral is gone, but the constant still there. What you end up with is

e-ctDelta(x-y-ct)
 
  • #5
cheers

from that, the integral goes into the second integral

u(x,t)=[INT (u(y, 0)dy*e^-ct*DELTA(x-y-ct))] - integrated over real space

does this equal e^-ct*u(x-ct,0) ?
 
  • #6
wolf party said:
cheers

from that, the integral goes into the second integral

u(x,t)=[INT (u(y, 0)dy*e^-ct*DELTA(x-y-ct))] - integrated over real space

does this equal e^-ct*u(x-ct,0) ?
Yes.
 

1. What is a Fourier transform?

A Fourier transform is a mathematical tool used to decompose a function into its constituent frequencies. It is commonly used in signal processing and allows for the analysis of periodic functions and signals in the frequency domain.

2. How do you solve a tricky integral using Fourier transforms?

To solve a tricky integral using Fourier transforms, you first need to express the function as a sum of sinusoidal functions using the Fourier series. Then, apply the Fourier transform to each term in the series and use the properties of the transform to simplify the integral. Finally, use the inverse Fourier transform to obtain the solution.

3. What are some common applications of Fourier transforms?

Fourier transforms have many applications in various fields such as engineering, physics, and mathematics. Some common applications include signal processing, image processing, data compression, and solving differential equations.

4. Can Fourier transforms be used for non-periodic functions?

Yes, Fourier transforms can also be applied to non-periodic functions. However, in this case, the transform is known as the Fourier integral transform and the function is expressed as an integral instead of a series.

5. Are there any limitations to using Fourier transforms?

While Fourier transforms are a powerful tool for solving integrals and analyzing functions, there are some limitations. For example, they cannot be used for functions that are not integrable or do not have a finite number of discontinuities. Additionally, they may not be suitable for functions with non-standard properties such as fractals.

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