Laplacian Vector: Definition & Derivation

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In summary, the conversation discusses the Laplacian of a vector and how it can be derived in different coordinate systems. The formula for the Laplacian is shown for an arbitrary curvilinear coordinate system, but it is noted that defining it in different coordinate systems would result in different answers. The conversation also touches on the process of solving the vector Laplacian and breaking it down into smaller components.
  • #1
pivoxa15
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In http://mathworld.wolfram.com/Laplacian.html under

'Using the vector derivative identity' It has that forumula for the laplacian of a vector. In cartesian coords it can be derived but I read in books that the this laplacian on vector fields that are not cartesian can only be defined. But why don't they define it in other systems as the scalar laplacian (wrt to their own coords systems) of each components of their vector field? It would make more sense wouldn't it?
 
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  • #2
If (u,v,w) is an arbitrary curvilinear coordinate system, then:

[tex]\nabla^2 \vec A = \nabla^2 (\hat e_u A_u + \hat e_v A_v + \hat e_w A_w ) = \nabla^2 (\hat e_u A_u) + \nabla^2 ( \hat e_v A_v) + \nabla^2 ( \hat e_w A_w )[/tex]

In a system where the basis vectors depended on position, there will be extra terms, eg:

[tex] \nabla^2 (\hat e_u A_u) = \hat e_u \nabla^2 A + 2 (\nabla \cdot \hat e_u) \nabla A_u + A_u \nabla^2 \hat e_u [/tex]

If you're asking why we don't just define the Laplacian so that:

[tex]\nabla^2 \vec A = \hat e_u \nabla^2 (A_u) + \hat e_v \nabla^2 (A_v)+ \hat e_w \nabla^2 (A_w)[/tex]

the reason is that the answer we'd get would depend on the coordinate system we're using, which is something we don't want.
 
  • #3
StatusX said:
If (u,v,w) is an arbitrary curvilinear coordinate system, then:

[tex]\nabla^2 \vec A = \nabla^2 (\hat e_u A_u + \hat e_v A_v + \hat e_w A_w ) = \nabla^2 (\hat e_u A_u) + \nabla^2 ( \hat e_v A_v) + \nabla^2 ( \hat e_w A_w )[/tex]

So is this equation equivalent to the (established) vector derivative identity given in MathWorld for any coordinate system?
 
  • #4
I don't know what you mean. All that equation used was the expansion of A in the (u,v,w) coordinate system and the linearity of the laplacian.
 
  • #5
I think my question arose from a misunderstanding on my behalf. Is it the case that the equation (highlighted in post 3) is the first step in solving the vector Laplacian of A. The next step is to apply the definition of a Laplacian vector on each component, like breaking a complicated problem into smaller bits but applying the same principle, which in this case is the definition of a Laplacian vector.

When you say the answer would depend on the coord system, do you mean that if I got in spherical coords, A=r(wrt r coord) than in cartesian, it should be A=(x,y,z). But this doesen't always happen if you (hypothetically) define the Laplacian as you have done in the last equation of post 2.
 
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What is a Laplacian vector?

A Laplacian vector is a mathematical operator or vector that is used to describe the rate of change of a scalar field in space. It is commonly denoted by the symbol ∇² and is also known as the Laplace operator.

How is a Laplacian vector defined?

The Laplacian vector is defined as the dot product of the gradient of a scalar field with itself. In mathematical notation, it can be written as: ∇²φ = ∇ · (∇φ), where φ is the scalar field and ∇ is the gradient operator.

What is the physical interpretation of a Laplacian vector?

The Laplacian vector describes the spatial variation of a physical quantity. It can be thought of as a measure of the curvature or smoothness of a field. A positive Laplacian vector indicates a region of increasing values, whereas a negative Laplacian vector indicates a region of decreasing values. A Laplacian vector of zero indicates a region of constant values.

How is a Laplacian vector derived?

The Laplacian vector is derived from the second derivative of a scalar field. By taking the second derivative, we can determine how the field changes in all directions, which is represented by the dot product of the gradient vector with itself. This operation results in the Laplacian vector.

What are some applications of the Laplacian vector?

The Laplacian vector has various applications in physics, engineering, and mathematics. It is commonly used in fluid dynamics, electromagnetism, and heat transfer to describe the behavior of physical quantities in space. It is also used in image processing and machine learning algorithms to identify patterns and detect edges in images.

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