Tensors: switching between mixed and contravariant components

In summary, tensors are mathematical objects that can be used to represent and manipulate multidimensional data. They can have mixed components, which are a combination of covariant and contravariant components, or they can have purely contravariant components. The process of switching between these two types of components involves using the metric tensor to raise or lower indices. This allows for the manipulation of tensors in different coordinate systems and makes them a powerful tool in various fields, such as physics and engineering.
  • #1
electricspit
66
4
I'm working on the electromagnetic stress-energy tensor and I've found this in a book by Landau-Lifshitz:

[itex]
T^{i}_{k} = -\frac{1}{4\pi} \frac{\partial A_{\ell}}{\partial x^{i}} F^{k\ell}+\frac{1}{16\pi}\delta^{k}_{i} F_{\ell m} F^{\ell m}
[/itex]

Becomes:

[itex]
T^{ik} = -\frac{1}{4\pi} \frac{\partial A^{\ell}}{\partial x_{i}} F^{k}_{\ell}+\frac{1}{16\pi}g^{ik} F_{\ell m} F^{\ell m}
[/itex]

I was wondering how this work? [itex]F^{ik}[/itex] is the electromagnetic field tensor, [itex]A_{\ell}[/itex] is the potential of the field.
 
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  • #2
Your first equation cannot be correct, you have switched the free covariant and free contravariant indices on the different sides of the equation. Let me assume that the LHS is instead ##T^k_i##. To obtain the second equation from the first, multiply on both sides with ##g^{in}##:
$$
T^{kn} \equiv g^{in} T^k_i =
-\frac{1}{4\pi} g^{in} F^{k\ell} (\partial_i A_\ell)
+\frac{1}{16\pi} g^{in}\delta^k_i F_{\ell m} F^{\ell m}
$$
Now use ##g^{in}## to raise the ##i## index and use ##A_ \ell B^\ell = A^\ell B_\ell## in the first term as well as ##g^{in} \delta^k_i = g^{kn}## in the second to rewrite this as
$$
T^{kn} =
-\frac{1}{4\pi} F^{k}_{\phantom k \ell} (\partial^n A^\ell)
+\frac{1}{16\pi} g^{kn} F_{\ell m} F^{\ell m}.
$$
Beware! Writing ##F^k_{\ell}## is dangerous. You must keep track of which index was lowered somehow since ##F## is anti-symmetric (which is why I have added the space in the subinex). Renaming ##n \to i## on both sides gives you the relation.

Note that I have used the notation ##\partial_i = \frac{\partial}{\partial x^i}## and ##\partial^i = \frac{\partial}{\partial x_i}##.
 
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  • #3
Ah yes sorry I made a typo, I should have read my post over more carefully.

Right, I figured it had something to do with [itex]g^{in}[/itex] but I wasn't quite sure, I always forget that I can use dummy indices to multiply through with these things.

I'm still not clear on how [itex]g^{in}[/itex] raises the [itex]i[/itex] index.

I see how the [itex]\ell[/itex] index was swapped, but I'm not sure about the [itex]i[/itex] in the first term.

Thank you for your help.

Edit: Sorry about not keeping track with the mixed indices, I don't even understand them well enough to do that.
 
  • #4
electricspit said:
I'm still not clear on how [itex]g^{in}[/itex] raises the [itex]i[/itex] index.

This is essentially by definition. If we have a contravariant vector with components ##A^k##, then ##g_{ik} A^k## are the components of a covariant vector, we can call it ##A_i##. In the same way we can relate the components of a covariant vector with those of a contravariant one by ##g^{km}##. The procedure is consistent due to ##g^{km}g_{ik} = \delta^m_i##. The same goes for raising and lowering indices of higher order tensors.

[Edit] For higher order tensors, you raise one individual index using one insertion of the metric. Since the indices generally are not equivalent, it is necessary to keep track of the index order in the original tensor unless it symmetric.
 
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  • #5
Right but what does a mixed tensor even mean?
 
  • #6
A mixed tensor is a tensor which is not contravariant or covariant, i.e., its components has both contravariant and covariant indices. If you are familiar with the nomenclature "tensor of type ##(n,m)##", a mixed tensor is a tensor of type ##(n,m)##, where both ##n## and ##m \geq 1##. See also http://en.wikipedia.org/wiki/Mixed_tensor
 
  • #7
So under specific transformations it acts in different ways?
 
  • #8
Under any transformations, some indices of a mixed tensor will transform covariantly and others contravariantly.
 
  • #9
I'd just go read the Wikipedia articles on this but I have and they were slightly confusing.

What is the importance of the distinction between the two?
 
  • #10
My first tensor book is Tensor Calculus by Kay. Very good for introduction. There are many which starts from even a simpler baseline.
The second one is Synge and Schild.
Try them!
(note: isn't this suppose to be in the calculus page?)
 
  • #11
electricspit said:
I'd just go read the Wikipedia articles on this but I have and they were slightly confusing.

What is the importance of the distinction between the two?

Thinking about it in terms of transformations of components, the components of a (n,m) tensor transform this way:

$$T'^{~i...}_{~~~~n...}=T^{a...}_{~~~q...}\frac{\partial x'^i}{\partial x^a}...\frac{\partial x^q}{\partial x'^n}$$

The contravariant indices transform with contravariant factors (primes above) and covariant indices transform with covariant factors (primes below).

Thinking about it in more geometric terms, a (n,m) tensor is a multi-linear function of n one-forms and m vectors into real numbers. It accepts n one-forms as arguments and m vectors as arguments and returns one single number:

$$T(\tilde{\omega},...,\vec{q},...)=T^{i...}_{~~n...}\omega_i...q^n... $$
 
  • #12
Right so thanks to TSC for the recommendation.

Matterwave:

I have no idea what a one form is. I feel like in my 3rd year of physics I should know?
 
  • #13
A one form is simply the dual to vectors. It is a linear function of vectors into numbers. In the old language it is called a covariant vector.
 
  • #14
Do you have any book recommendations on this subject? I don't know what dual means really.
 
  • #15
There are 2 approaches to learning tensors. The component approach and the component-free approach.
In general for physics and engineering students, the component approach is easier to start with. The one-form is the component-free approach.
The 2 books I recommend is the component approach. It is better, I think, for your purpose.(covariant electromagnetism)
 
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FAQ: Tensors: switching between mixed and contravariant components

What are tensors?

Tensors are mathematical objects that are used to represent physical quantities that have magnitude and direction, such as velocity, force, and stress. They are used extensively in physics, engineering, and mathematics to describe the relationships between different physical quantities.

What is the difference between mixed and contravariant components of a tensor?

Mixed components of a tensor refer to the components that have both upper and lower indices, while contravariant components refer to the components that only have upper indices. Mixed components represent physical quantities that transform differently under coordinate transformations, while contravariant components represent physical quantities that transform in the same way as the coordinate system.

How do you switch between mixed and contravariant components of a tensor?

To switch between mixed and contravariant components of a tensor, you can use the metric tensor. The metric tensor is a mathematical tool that allows you to raise or lower indices of a tensor. By contracting the metric tensor with the mixed components of a tensor, you can obtain the contravariant components, and vice versa.

What is the importance of understanding how to switch between mixed and contravariant components of a tensor?

Understanding how to switch between mixed and contravariant components of a tensor is crucial in many areas of physics and engineering, especially in the fields of relativity, electromagnetism, and fluid mechanics. It allows you to correctly describe and manipulate physical quantities in different coordinate systems and to solve complex problems that involve tensors.

Are there any practical applications of tensors in real life?

Yes, there are many practical applications of tensors in real life. Tensors are used in engineering to design structures, in physics to predict the behavior of materials, and in computer graphics to create realistic animations. They are also used in machine learning and data analysis to represent and process large datasets. Tensors play a crucial role in many modern technologies, such as GPS, MRI machines, and image recognition software.

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