Vector Calculus Theorems - Duality Question

In summary, the conversation discusses the use of vector analysis and differential and integral forms in understanding vector identities and theorems. The three integral theorems, namely the Gradient, Curl, and Divergence Theorems, are discussed and compared to their derivative counterparts. The conversation also touches on the Laplacian and its relevance to these theorems. However, there is no clear consensus on what could be a suitable theorem for the Laplacian in this context.
  • #1
bolbteppa
309
41
I'm trying to go over some vector analysis using forms & kind of noticed what look like random vector identities are more appropriately thought of, to me at least, as differential analogues of the classical integral theorems in the way Maxwell's equations can be cast in differential & integral form. However I'm missing a theorem:


Integral Gradient Theorem: [itex] \smallint_{\vec{a}}^{\vec{b}} \nabla f \cdot d \vec{r} = f(\vec{b}) - f(\vec{a})[/itex]

Integral Curl Theorem: [itex]\smallint_S (\nabla \times \vec{F}) \cdot \hat{n} dS = \smallint_{\partial S} \vec{F} \cdot d \vec{r}[/itex]

Integral Divergence Theorem: [itex]\smallint_V \nabla \cdot \vec{F} dV = \smallint_{\partial V} \vec{F} \cdot \hat{n} dS[/itex]

---

Derivative Gradient Theorem:

Derivative Curl Theorem: [itex]\nabla \times (\nabla \phi) = 0[/itex]

Derivative Divergence Theorem: [itex]\nabla \cdot (\nabla \times \vec{F}) = 0[/itex]

---

Any ideas as to what I should put in there?

Also, could one bluff an appropriate differential & integral theorem for the scalar or vector laplacians?
 
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  • #2
Well I'm not really sure, but by looking at the Derivative Curl and Divergence theorems, the pattern seems to be:

Derivative [operator] Theorem: [operator]( some expression ) = 0,

where (some expression) is replaced by something that guarantees the right-hand integral is 0 (for any general F of this form) in the above 3 equations.
For the curl and the divergence theorems, the form of [itex]\vec{F}[/itex] is respectively a gradient and a curl.

The most obvious continuation (for the gradient theorem) would be the constant vector functions, since [itex]f(\vec{b})-f(\vec{a}) = 0[/itex] then. So my guess would be that the Derivative gradient theorem is:
[itex]\nabla (const) = \vec{0}[/itex] which is fairly trivial.

By the way, I don't think the derivative [operator] theorems are equivalent to their integral counterparts, because the derivative theorems are simply vector identities that can be checked from the definition to be 0, while the integral theorems must work even if the integrals are not 0.
EDIT: This page has a lot of identities. Maybe you'll find a more satisfactory choice here http://en.wikipedia.org/wiki/Vector_calculus_identities
 
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  • #3
Maybe you are thinking of Maxwell's equations in their integral and differential versions (which are equivalent).
But they are proven equivalent because of the integral theorems (Green's, Stokes's and Divergence Theorems), not the 3 other identities. Or at least, for the major part of the proof.

Oh and for the laplacian integral theorems, I think Green's identities are relevant.
 
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  • #4
One moves between the differential & integral form of Maxwell's equations via Stokes & Gauss, so on face value if you can't think of the theorems in my post as dual to each other you can't think of the differential & integral form of Maxwell's equations as dual either right?

Apparently either a constant function or the zero function work, depending on whether you're thinking in terms of cohomologies or reduced cohomologies :tongue: I was just hoping there was something a bit more substantial you could put in it's place I guess...

If we could find a way to fit the Laplacian into all of this it would be really nice, but I don't see what to do with it right now...
 
  • #5
This a a problem in the traditional vector calculus. We have many variations of each formula.
Your first three are examples of the fundamental theorem of calculus (Stokes theorem)
$$\int_{\partial \Omega} \omega=\int_\Omega \mathrm{d}\omega$$
The other two are examples of exact forms being closed
$$\mathrm{d}^2\omega=0$$
you can dress it as an integral if you like
$$\int_{\partial \Omega} \mathrm{d}\omega=\int_{\partial^2 \Omega} \omega=\int_{ \Omega} \mathrm{d}^2\omega=0$$
I do not think of these as dual because the second set have one more derivative.
That is the reason there are two instead of three.
The Laplacian arises form
$$\Delta=d\delta+\delta d$$
where δ is the adjoint of d
 

1. What is duality in vector calculus theorems?

Duality in vector calculus theorems refers to the concept that some theorems have two versions, one involving vectors and the other involving dual vectors or covectors. This allows for a more general and versatile understanding of the theorem.

2. Why is duality important in vector calculus?

Duality is important in vector calculus because it allows for a more comprehensive understanding of the relationships between vectors and their dual counterparts. It also helps to simplify and generalize certain theorems, making them applicable to a wider range of problems.

3. Can you provide an example of duality in vector calculus theorems?

One example of duality in vector calculus is the Gradient theorem, which has two versions - one involving the gradient vector and the other involving the divergence of a vector field. These two versions are equivalent and can be used interchangeably in different situations.

4. How does duality affect the application of vector calculus theorems?

Duality can affect the application of vector calculus theorems by providing alternative ways to solve problems and allowing for a deeper understanding of the underlying concepts. It also allows for the use of different mathematical tools and techniques to approach a problem.

5. Are there any limitations to duality in vector calculus theorems?

While duality is a useful concept in vector calculus, it is not applicable to all theorems. Some theorems may only have one version or may not have a dual version that is as straightforward to understand. Additionally, duality may not always provide a better or simpler solution to a problem.

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