Partial derivative of convolution integral

In summary, the conversation is discussing how to take the partial derivative of a convolution integral and whether or not it is necessary to have g(τ) inside the integral. The person asking the question also mentions their experience with performing the convolution integral in a simulation and their goal of expressing the partial derivative analytically before simulating it.
  • #1
cdsi385
1
0
Does anyone know how to take the partial derivative of a convolution integral where the derivative is taken with respect to one of the functions of the convolution integral?

In the following example, the best I can come up with is:

[itex]\frac{\partial}{\partial g(t)}\int L(t-\tau)g(t)\,d\tau=\int L(t-\tau)\,d\tau[/itex]

Is this correct, or does it even make sense?

To put this in context, what I usually do (successfully) is perform the convolution integral in a simulation (without the partial differentiation) where [itex]L(t)[/itex] is the impulse response function of a system and [itex]g(t)[/itex] is the velocity of my system which is calculated on the fly during the simulation.

What I'm trying to do now is make a new simulation which relies on this partial derivative which I'm trying to express analytically before simulating it. If what I've expressed above is correct then all I need to simulate is: [itex]\int L(t-\tau)\,d\tau[/itex]

Thanks in advance...
cdsi385
 
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  • #2
To be a convolution, it should have g(τ) inside the integral, not g(t).
 

1. What is the definition of a partial derivative of a convolution integral?

The partial derivative of a convolution integral is a mathematical concept used to find the rate of change of a convolution integral with respect to one of its variables. It is denoted by the symbol ∂ and is calculated by holding all other variables constant while taking the derivative.

2. How is the partial derivative of a convolution integral used in scientific research?

The partial derivative of a convolution integral is used in various fields of science, such as physics, engineering, and signal processing. It is used to analyze the behavior of systems with multiple variables and to solve differential equations that involve convolution integrals.

3. Can you give an example of a real-world application of the partial derivative of a convolution integral?

One example is in image processing, where the partial derivative of a convolution integral is used to calculate the gradient of an image. This information can then be used for edge detection, feature extraction, and other image analysis tasks.

4. What is the difference between a partial derivative and a total derivative of a convolution integral?

The partial derivative of a convolution integral only considers the change in one variable while holding all others constant, whereas the total derivative takes into account the effect of changes in all variables. In other words, the partial derivative is a local measure of change, while the total derivative is a global measure of change.

5. Are there any limitations or assumptions when using the partial derivative of a convolution integral?

One limitation is that the partial derivative only applies to functions that are continuous and differentiable. Additionally, it assumes that the variables involved are independent of each other, and the function being evaluated is well-behaved. It is important to consider these limitations when applying the partial derivative in scientific research.

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