Is my understanding of the second-rank four-tensor correct?

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In summary, the four-tensor is a 4x4 matrix Aik with the property that for some transformation αik, elements of Aik are related to elements of the tensor A'ml in the transformed coordinates by the equation Aik = αimαklA'ml.
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jack476
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I just started reading Landau and Lifschitz' Classical Theory of Fields today and I reached the section on the spacetime four-tensor.

The explanation given was that the four-tensor is a 4x4 matrix Aik with the property that for some transformation αik, elements of Aik are related to elements of the tensor A'ml in the transformed coordinates by the equation Aik = αimαklA'ml.

Am I correct in my understanding that this means that element aik of Aik is related to element a'ml of Aml, for some i, k, m, l, by the product αimαkl?

So if I have, for instance, a'23 and I want to find a14, then i = 1, k = 4, m = 2, and l = 3, so I multiply a'23 by the product of the elements α12 and α43 of the transformation matrix α? Or am I misunderstanding that?
 
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Not quite. Using your notation, the basis-conversion equation is

$$a_{ik}=a'_{ml}\alpha_i^m\alpha_k^l$$

This looks like it's a product, but it's actually a sum of products, because under the Einstein Summation Convention we sum over matching subscript-superscript pairs. So it actually means:

$$a_{ik}=\sum_{m=1}^n\sum_{l=1}^n a'_{ml}\alpha_i^m\alpha_k^l$$

That's why I changed the ##m## and ##l## from subscripts to superscripts, by the way.

Also, the tensor is not a matrix. The matrix is a representation of the tensor in a given basis. This might seem like a quibble, but it will help a great deal with understanding as your reading goes on. One of the most important properties of tensors is that they are not dependent on the choice of basis of the underlying vector space.
 
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As said before, the tensor is not a matrix but rather a geometric object (for ex, a vector is not the same as its components). One way to think of tensors, without defining the coordinate change rules, is as a multilinear map from elements of a vector space to the reals.
 

Related to Is my understanding of the second-rank four-tensor correct?

What is a second-rank four-tensor?

A second-rank four-tensor is a mathematical object that has two subscripts and two superscripts, representing its four indices. It is used to represent the relationship between two vectors in three-dimensional space.

How is a second-rank four-tensor different from a second-rank tensor?

A second-rank tensor only has two indices, while a second-rank four-tensor has four indices. This makes it more complex and allows it to represent a wider range of relationships between vectors.

What does it mean for a second-rank four-tensor to be "correct"?

In this context, "correct" means that the tensor is accurately representing the relationship between the two vectors. This can be determined by checking if the tensor satisfies certain mathematical properties and equations.

How is a second-rank four-tensor used in scientific research?

Second-rank four-tensors are commonly used in fields such as physics and engineering to describe the stress and strain relationships in materials. They can also be used in other areas of mathematics and science where the relationship between two vectors needs to be represented.

Can a second-rank four-tensor be visualized?

Yes, a second-rank four-tensor can be visualized using diagrams or graphs. However, these visualizations can be quite complex and may only provide a partial understanding of the tensor. It is more commonly represented and understood through its mathematical equations and properties.

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