A fundamental solution and its derivatives

In summary: If I have a fundamental solution f to the Laplace equation, then the gradient of f is also a solution to the Laplace equation. I can see why if I express Laplace equation in Cartesian coordinates, and see they the operators commutes in Cartesian coordinates. But is there a coordinate-free way to see this?Are you working in 3-space? Strictly speaking the Laplacian of a vector and of Laplacian of a scalar are not identical, but it is a minor concern. All the usual vector operators like Laplacian, gradient, curl and divergence should commute since they do not have spatial dependence.Recall the following basic identitiesLapl
  • #1
hanson
319
0
Hello, if I have a fundamental solution, ,f, to a partial differential equation L(f)=0, where L is the differential operator, is that true that the derivatives of the fundamental solution, like D(f), will also be solution to the partial differential equation?

Intuitively, is it because things are linear, so I can always interchange the derivatives with the original differential operator of the PDE: L(D(f))=D(L(f))=0?
 
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  • #2
It is not for the most part to do with linearity. As you say it depends on the commutator of the operator with differentiation.
Is [L,D]=LD-DL=0?
Consider some examples from ODE.
D[D^2+3D-7]=[D^2+3D-7]D=[D^3+3D^2-7D]
They commute so if f is a solution Df is as well.
what about xD-1?
D[xD-1]=xD^2+D
[xD-1]D=xD^2-D
These are not equal, the operators do not commute.
f=x is a solution of [xD-1]f=0
but Dx=1 is not.
 
  • #3
lurflurf said:
It is not for the most part to do with linearity. As you say it depends on the commutator of the operator with differentiation.
Is [L,D]=LD-DL=0?
Consider some examples from ODE.
D[D^2+3D-7]=[D^2+3D-7]D=[D^3+3D^2-7D]
They commute so if f is a solution Df is as well.
what about xD-1?
D[xD-1]=xD^2+D
[xD-1]D=xD^2-D
These are not equal, the operators do not commute.
f=x is a solution of [xD-1]f=0
but Dx=1 is not.

Thanks for your reply. I think I get it, but why
D[D^2+3D-7]=[D^3+3D^2-7D],
but
D[xD-1]=xD^2+D ?
Should D[xD-1]=xD^2+D-D = xD^2?
 
  • #4
hanson said:
Thanks for your reply. I think I get it, but why
D[D^2+3D-7]=[D^3+3D^2-7D],
but
D[xD-1]=xD^2+D ?
Should D[xD-1]=xD^2+D-D = xD^2?
I had written out a long response until I realized I had read it backwards! You are completely correct.

D[xD- 1]= D(xD)- D and, using the product rule, that is [itex]D(x)D+ xD(D)- D= D+ xD^2- D= xD^2[/itex]. "[itex]D[xD- 1]= xD^2- D[/itex]" is wrong. You can "factor" constants out of the differential expression but not functions of the variable.
 
  • #5
HallsofIvy said:
I had written out a long response until I realized I had read it backwards! You are completely correct.

D[xD- 1]= D(xD)- D and, using the product rule, that is [itex]D(x)D+ xD(D)- D= D+ xD^2- D= xD^2[/itex]. "[itex]D[xD- 1]= xD^2- D[/itex]" is wrong. You can "factor" constants out of the differential expression but not functions of the variable.

Thank you very much!
 
  • #6
Let's focus on the Laplace equation. Is there a good way to understand the following?
If I have a fundamental solution f to the Laplace equation, then the gradient of f is also a solution to the Laplace equation. I can see why if I express Laplace equation in Cartesian coordinates, and see they the operators commutes in Cartesian coordinates. But is there a coordinate-free way to see this?
 
  • #7
Are you working in 3-space? Strictly speaking the Laplacian of a vector and of Laplacian of a scalar are not identical, but it is a minor concern. All the usual vector operators like Laplacian, gradient, curl and divergence should commute since they do not have spatial dependence.
Recall the following basic identities
Laplacian(vector)=grad(div(vector))-curl(curl(vector))
Laplacian(scalar)=div(grad(scalar))
curl(grad(scalar))=0

so if we have for scalar f
Laplacian(f)=
Laplacian(grad(f))=grad(div(grad(f)))-curl(curl(grad(f)))=grad(0)-curl(0)=0
 
  • #8
lurflurf said:
Are you working in 3-space? Strictly speaking the Laplacian of a vector and of Laplacian of a scalar are not identical, but it is a minor concern. All the usual vector operators like Laplacian, gradient, curl and divergence should commute since they do not have spatial dependence.
Recall the following basic identities
Laplacian(vector)=grad(div(vector))-curl(curl(vector))
Laplacian(scalar)=div(grad(scalar))
curl(grad(scalar))=0

so if we have for scalar f
Laplacian(f)=
Laplacian(grad(f))=grad(div(grad(f)))-curl(curl(grad(f)))=grad(0)-curl(0)=0

Thanks lurflurf. Let me read it in detail.
 

FAQ: A fundamental solution and its derivatives

What is a fundamental solution and its derivatives?

A fundamental solution is a type of function used in mathematical analysis, specifically in partial differential equations. It is a solution to a particular equation that represents the response of a system to a unit impulse. Derivatives of a fundamental solution refer to the function's higher-order derivatives, which can be used to solve more complex equations.

Why are fundamental solutions and their derivatives important?

Fundamental solutions and their derivatives are important because they allow us to solve complex equations by breaking them down into simpler, more manageable equations. They also serve as a basis for many mathematical techniques and models in fields such as physics, engineering, and finance.

How are fundamental solutions and their derivatives used in real-world applications?

Fundamental solutions and their derivatives are used in a wide range of real-world applications, such as modeling heat transfer in engineering, predicting stock market trends in finance, and understanding the behavior of waves in physics. They are also used in image processing and medical imaging to enhance and analyze images.

Can fundamental solutions and their derivatives be generalized to higher dimensions?

Yes, fundamental solutions and their derivatives can be generalized to higher dimensions, such as in three-dimensional space or even higher dimensions. The concept and properties of fundamental solutions remain the same, but the equations and calculations become more complex.

Are there any limitations to using fundamental solutions and their derivatives?

While fundamental solutions and their derivatives are powerful tools in solving equations and modeling real-world phenomena, they have limitations. These include the assumption of linearity in the underlying equation, the need for certain boundary conditions to be met, and the possibility of unstable solutions in some cases.

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