Undergrad Manipulation of 2nd, 3rd & 4th order tensor using Index notation

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The discussion focuses on expressing a tensor equation using index notation, specifically for a fourth-order tensor A and third-order tensor C, among others. It highlights the misunderstanding of tensor operations, noting that concepts like "transpose" and "inverse" do not apply to tensors in the same way they do to matrices. Participants emphasize the importance of ensuring the validity of tensor equations before converting them to index notation. Resources such as Wikipedia articles on tensor notation and contraction are recommended for further understanding. The conversation underscores the complexities of tensor theory and the need for clarity in notation and operations.
chowdhury
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If I have an equation, let's say,
$$\mathbf{A} = \mathbf{B} + \mathbf{C}^{Transpose} \cdot \left( \mathbf{D}^{-1} \mathbf{C} \right),$$
1.) How would I write using index notation? Here
  • A is a 4th rank tensor
    [*]B is a 4th rank tensor
    [*]C is a 3rd rank tensor
    [*]D is a 2nd rank tensor


I wrote it as $$A_{ijkl} = B_{ijkl} + C_{ijk}^{Transpose} D_{ll}^{-1} C_{ijk} $$
$$A_{ijkl} = B_{ijkl} + C_{kij} D_{ll}^{-1} C_{ijk} $$

2.) How to denote the transpose of a third rank tensor? $$C^{Transpose}$$ or $$C^{t}$$ or is there a succinct way of writing it?
 
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Tensor theory does not include a notion of "transpose", which is a concept that applies only to matrices.
Nor does it have a notion of an inverse tensor, so I don't know what to make of your ##\mathbf D^{-1}##. Again that is a concept that applies only to matrices.
Lastly, I don't know what you mean by the dot before the parenthesis. Do you mean a dot product (inner product)? If so, that's probably not defined in this context. Alternatively, perhaps you mean the tensor product, but that's usually written with the ##\otimes## symbol.

In summary, you need to make sure first that you've written a valid tensor equation before you start trying to rewrite it in index notation.
 
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andrewkirk said:
Do you mean a dot product (inner product)?
Yes. He is reading about piezoelectricity (chapter 8) in B. A. Auld, Acoustic fields and waves in solids, vol. 1, 1973.
https://www.physicsforums.com/threads/inverse-of-a-vector.1012428/

@chowdhury You should really read those articles first:
https://en.wikipedia.org/wiki/Einstein_notation
https://en.wikipedia.org/wiki/Raising_and_lowering_indices
https://en.wikipedia.org/wiki/Tensor_contraction
https://en.wikipedia.org/wiki/Covariant_transformation
https://en.wikipedia.org/wiki/Tensor_field

Even
https://www.physicsforums.com/insights/what-is-a-tensor/
should help. And the Wikipedia article on piezoelectricity has some of those formulas, too, and written in a more modern way:
https://en.wikipedia.org/wiki/Piezoelectricity#Mathematical_description
 
@andrewkirk and @fresh_42,

I indeed read a book of 300 pages on basic tensor notation applied to mechanics, and what I could grasp as per my understanding, I am writing here.

Just to reiterate my understanding:
  • by matrix, we mean (m x n). As a sidenote in Matlab, you can have matrices of ( m x n x L x ...), I think it is called paging.
    [*]Vector (n x 1) or (1 x n)

I learned today, from @andrewkirk, that tensor theory (more than second rank) does not include the concept of transpose and inverse. Thank you.

@fresh_42 : I indeed read few of the wikipedia articles you have provided, but you know, in wiki, many people write many things, not sure whether it is a typo or not. This is a great forum, where I can learn from expert, like you, directly. Thanks for being patient.
 
Last edited:
fresh_42 said:
Yes. He is reading about piezoelectricity (chapter 8) in B. A. Auld, Acoustic fields and waves in solids, vol. 1, 1973.
https://www.physicsforums.com/threads/inverse-of-a-vector.1012428/

@chowdhury You should really read those articles first:
https://en.wikipedia.org/wiki/Einstein_notation
https://en.wikipedia.org/wiki/Raising_and_lowering_indices
https://en.wikipedia.org/wiki/Tensor_contraction
https://en.wikipedia.org/wiki/Covariant_transformation
https://en.wikipedia.org/wiki/Tensor_field

Even
https://www.physicsforums.com/insights/what-is-a-tensor/
should help. And the Wikipedia article on piezoelectricity has some of those formulas, too, and written in a more modern way:
https://en.wikipedia.org/wiki/Piezoelectricity#Mathematical_description

@andrewkirk Is the notation in Wikipedia correct? Comparing the dyadic product, it does not seem to be correct.

https://en.wikipedia.org/wiki/Einstein_notation

To me, this ##v## seems not right, as it results a scalar.
$$ v = v^{i} e_{i} =
\begin{bmatrix}
e_{1} & e_{2} & \cdots & e_{n}
\end{bmatrix} \begin{bmatrix}
v^{1}\\
v^{1}\\
\vdots \\
v^{n}
\end{bmatrix}
$$

$$ w = w_{i} e^{i} =
\begin{bmatrix}
w_{1} & w_{2} & \cdots & w_{n}
\end{bmatrix} \begin{bmatrix}
e^{1}\\
e^{1}\\
\vdots \\
e^{n}
\end{bmatrix}
$$

It is mentioned in the above wikipedia article

dyadic product is ##A^{i}_{j} = u^{i} v_{j} = (uv)^{i}_{j}##
 
The notation is correct. Under the convention, when the same letter is used for a lower and an upper index in a product, we sum over that index variable, so ##u^iv_i## denotes ##\sum_i u^i v_i##, a scalar. Assuming ##\mathbf u## is a row vector and ##\mathbf v## is a column vector, that is the same as the product ##\mathbf u \mathbf v##, ie the row vector goes first.

If you don't want to add the terms, you must use different indices, ##u^i v_j## That can be represented by a matrix. Again assuming ##\mathbf u## is a row vector and ##\mathbf v## is a column vector, that is the same as the product ##\mathbf v \mathbf u##, ie the column vector goes first.
 
Thread 'How to define a vector field?'
Hello! In one book I saw that function ##V## of 3 variables ##V_x, V_y, V_z## (vector field in 3D) can be decomposed in a Taylor series without higher-order terms (partial derivative of second power and higher) at point ##(0,0,0)## such way: I think so: higher-order terms can be neglected because partial derivative of second power and higher are equal to 0. Is this true? And how to define vector field correctly for this case? (In the book I found nothing and my attempt was wrong...

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