Derivative of a Convolution: Solving an ODE with an Integral Solution

In summary, the conversation discusses verifying that a particular solution satisfies a given ODE. The form of the solution involves an integral with respect to r, but in order to differentiate it with respect to t, the variable needs to be changed. One method is to use a generic equation for v(t) and substitute t+δt for t consistently, then take the difference and let δt tend to zero to obtain v'. This is the standard process of differentiation.
  • #1
Shaybay92
124
0
Hi,

I want to verify that the form of a particular solution satisfies the following ODE:

v' + (b/m)v = u/m

with

vpart= ∫e-(b/m)(t-r) (u(r)/m) dr

where the limits are from 0 to t

So I tried to differentiate v with respect to t, in order to substitute it back into the equation. But, how do you do that when the integral is with respect to r? Is there a need to change variables? How can you do this?

Cheers
 
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  • #2
Shaybay92 said:
Hi,

I want to verify that the form of a particular solution satisfies the following ODE:

v' + (b/m)v = u/m

with

vpart= ∫e-(b/m)(t-r) (u(r)/m) dr

where the limits are from 0 to t. So I tried to differentiate v with respect to t,... How can you do this?
v(t) = ∫tf(t, r).dr
v(t+δt)= ∫t+δtf(t+δt, r).dr
= ∫tf(t+δt, r).dr + ∫tt+δtf(t+δt, r).dr
So v' = ∫t(d/dt)f(t, r).dr + f(t, t)
 
  • #3
Sorry I'm not familiar with your method. I don't understand why you substitute "t+δt" for t. What approach are you using here? Could you elaborate or direct me to some further reading?

Cheers :)
 
  • #4
Shaybay92 said:
Sorry I'm not familiar with your method. I don't understand why you substitute "t+δt" for t. What approach are you using here? Could you elaborate or direct me to some further reading?

Cheers :)
The equation you posted for v(t) is generic - i.e. it's true for all t. So it's true both for a given t and for a later time t+δt. So you can write a second equation substituting t+δt for t consistently. Taking the difference, diving by δt, then letting δt tend to zero gives you v'. That is the standard process of differentiation.
 
  • #5
Oh I see what you mean. Thanks for the clarification. I'm just not use to this notation :)
 

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

The derivative of a convolution is the mathematical operation that calculates the rate of change of the output of a convolution with respect to its input. In other words, it measures how much the output of a convolution changes when the input is changed by a small amount.

2. Why is the derivative of a convolution important in scientific research?

The derivative of a convolution is important because it allows us to analyze and understand the behavior of a signal or system in response to changing inputs. This is crucial in many fields of science, such as signal processing, image processing, and machine learning.

3. How is the derivative of a convolution calculated?

The derivative of a convolution is calculated using the convolution theorem, which states that the derivative of a convolution is equal to the convolution of the derivative of the two functions. This can be expressed mathematically as d/dx(f * g) = (df/dx) * g + f * (dg/dx), where * represents the convolution operation.

4. Can the derivative of a convolution be negative?

Yes, the derivative of a convolution can be negative. The sign of the derivative depends on the shape and characteristics of the input functions and their overlapping region. A negative derivative indicates a decreasing output, while a positive derivative indicates an increasing output.

5. What are some applications of the derivative of a convolution in science?

The derivative of a convolution has many applications in science, including signal processing, image processing, data analysis, and machine learning. It is used to analyze the frequency and time characteristics of signals, to detect patterns and features in images, and to train and optimize machine learning models.

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