Help Using Heat Kernel and Convolutions

In summary, the solution of the heat equation with initial temperature distribution f is given by the convolution of f with the heat kernel. The LHS of the equation is the general solution, while the RHS is the specific solution for a given initial temperature distribution. The constants on the LHS determine the specific form of the heat kernel, but they are not used in the actual convolution calculation. It is important to practice using the convolution integral in order to become more comfortable with solving problems involving the heat equation.
  • #1
erok81
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My text gives the following definition for the solution of the heat equation with initial temperature distribution f is the convolution of f with the heat kernel.


[tex]u(x,t)= \frac{1}{c \sqrt{2t}}e^{-x^{2}/4c^{2}t} \ast f~=~ \frac{1}{2c \sqrt{\pi t}} \int_{-\infty}^{\infty}f(s)e^{-(x-s)^{2}/4c^{2}t}~ds[/tex]

I am confused how to use this. Most of the practice problems I tried only used the RHS of the above equation. I tried solving the same problems only using the LHS but didn't get anything close (but I am sure I was doing that part incorrectly). There is another example in the solution manual where they seem to use only the constants from the LHS and add them into the RHS equation. I've attached a screen shot of that case.

So does one use the stuff on the LHS? Maybe there are cases I haven't seen that use it, but for now I've done most of the problems in the text and haven't use it yet. :confused:

I may have messed up somewhere but in my example one image I was able to solve that and get the correct answer without using the convolution. Only the stuff on the RHS of my given formula.

Example two is the most confusion. Here they say to use (4) which i gave above. With this one they seem to pull out the constants and drop the rest.

Example two the actual problem shows...

[tex] \frac{\partial u}{\partial t}=\frac{\partial ^{2} u}{\partial ^{2} x}, ~u(x,0)=x^2[/tex]

So I thought that maybe they take the LHS stuff and use t=0 but then I get a singularity in my exponent stuff.

Hopefully this post makes sense. I might be trying to cram too much into a single post. Let me know if you need any clarification and I'll do my best.
 

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  • #2
Thanks!

Thank you for your question. The solution of the heat equation with initial temperature distribution f is indeed given by the convolution of f with the heat kernel. However, it is important to note that the LHS of the equation you provided is the general solution for the heat equation, while the RHS is the specific solution for the given initial temperature distribution f.

In order to solve a specific problem using the heat equation, you will need to use the RHS of the equation, which involves the convolution integral. The constants on the LHS are used to determine the specific form of the heat kernel for a given problem, but they are not used in the actual convolution calculation.

In the example you provided, the problem involves a specific initial temperature distribution (x^2) and the constants from the LHS are used to determine the specific form of the heat kernel. However, in order to actually solve the problem, you will need to use the convolution integral on the RHS.

It is important to note that the convolution integral may seem daunting at first, but with practice it becomes easier to use. I would suggest practicing with more problems that involve different initial temperature distributions to become more comfortable with the convolution process.

I hope this clarifies your confusion. If you have any further questions, please don't hesitate to ask. Good luck with your studies!
 

What is the Heat Kernel and how is it used in convolutions?

The Heat Kernel is a mathematical function used to describe the diffusion of heat in a given space. In image processing, it is used as a filter to smooth out noise and enhance edges in an image.

Why are convolutions useful in image processing?

Convolutions allow for the extraction of important features from an image, such as edges and textures, while also reducing noise. This makes them useful for tasks such as image enhancement and object recognition.

How do I choose the appropriate kernel size for my convolution?

The kernel size should be chosen based on the size of the features you want to extract from the image. Smaller kernel sizes are better for detecting fine details, while larger kernel sizes are better for capturing broader features.

Can I use the same kernel for all types of images?

No, the appropriate kernel will vary depending on the type of image and the specific features you are trying to extract. For example, a kernel that works well for detecting edges in a natural landscape image may not be as effective for detecting edges in a medical image.

What is the difference between 2D and 3D convolutions?

2D convolutions are used for processing images, while 3D convolutions are used for processing volumetric data, such as MRI scans. 3D convolutions take into account the depth or thickness of the data, while 2D convolutions only consider height and width.

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