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

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SUMMARY

The discussion focuses on the manipulation of tensors using index notation, specifically addressing the equation $$\mathbf{A} = \mathbf{B} + \mathbf{C}^{Transpose} \cdot \left( \mathbf{D}^{-1} \mathbf{C} \right)$$. Participants clarify that tensor theory does not include concepts like "transpose" or "inverse" for tensors beyond second rank. The correct representation in index notation is $$A_{ijkl} = B_{ijkl} + C_{kij} D_{ll}^{-1} C_{ijk}$$. Additionally, the importance of ensuring valid tensor equations before rewriting them in index notation is emphasized.

PREREQUISITES
  • Understanding of tensor ranks: 2nd, 3rd, and 4th rank tensors
  • Familiarity with index notation and Einstein summation convention
  • Knowledge of tensor operations such as contraction and tensor products
  • Basic principles of piezoelectricity as related to tensor applications
NEXT STEPS
  • Study "Einstein Notation" to grasp tensor representation and manipulation
  • Learn about "Raising and Lowering Indices" in tensor calculus
  • Explore "Tensor Contraction" for understanding tensor operations
  • Investigate "Covariant Transformation" to comprehend tensor behavior under coordinate changes
USEFUL FOR

Researchers, physicists, and engineers working with advanced tensor calculus, particularly in fields such as material science and continuum mechanics, will benefit from this discussion.

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
 
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@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.
 

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