Transformation matrix on tensors

In summary, the conversation discusses transforming a rank 2 tensor from one coordinate reference system to another using a transformation matrix. The suggested operation for transforming the tensor is to multiply the inverse of the transformation matrix by the original tensor and then multiply the result by the transformation matrix. It is also noted that this operation can be thought of as multiplying the matrices representing the tensor and transformation. Additionally, it is mentioned that even if the tensor cannot be represented as a matrix, it can still be transformed using linear maps.
  • #1
kernelinho
1
0
Hello.

I wasn't sure whether to post this here on in some of the physics sections.

I have a rank 2 tensor in one coordinate reference system [x1, x2, x3], the one where only the principal elements are non zero: R=[ a11 0 0; 0 a22 0; 0 0 a33 ].

I want the tensor R in some other orthogonal coordinate reference system. I have the transformation matrix U from the system [x1, x2, x3] to the second one [X1, X2, X3].

I know how to use U to transform vectors from one system to the other:

[V1; V2; V3]= U [v1; v2; v3]

But I don't know what operation to do to transform a tensor. I'm led to believe that it could be something like

[R(in Xi)] = U^-1 R(in xi) U

But I'm not sure whether this is right nor what's the rationale for it.

I would appreciate any help you could give me.
 
Physics news on Phys.org
  • #2
Same thing. Think of the tensor and transformation as matrices and multiply the matrices.
 
  • #3
In addition, even if the tensor was not representable as a matrix (two dimensional rectangular array of numbers), you still have the functional representation, T(v) is a multilinear map from some space V to some space W, and you have linear maps C and C' from V to V' and W to W' respectively, where V' and W' are identical to V and W except for the coordinate maps for their elements. Then C'(T(C-1v)) is T with the coordinate change applied, as T can only act on objects in V and transforms them to W objects. The C's take care of translating the objects from V' and into W', where if v is a multivector, the linear transformations are distributed appropriately. By linearity, we have C'TC-1 as the proper tensor, as you have already surmised.
 

1. What is a transformation matrix on tensors?

A transformation matrix on tensors is a mathematical tool used to represent linear transformations of tensors, which are multi-dimensional arrays of numbers or vectors. It is a square matrix that can be multiplied with a tensor to produce a new tensor with the same dimensions but with different values.

2. How do you apply a transformation matrix on tensors?

To apply a transformation matrix on tensors, you first need to multiply the matrix with the tensor using matrix multiplication. This will result in a new tensor with the same dimensions as the original, but with its values transformed according to the matrix. The order of multiplication is important, as matrix multiplication is not commutative.

3. What is the purpose of using a transformation matrix on tensors?

The main purpose of using a transformation matrix on tensors is to simplify and generalize the process of performing linear transformations on multi-dimensional arrays. It allows for easier manipulation and analysis of tensors, especially in fields such as physics, engineering, and data science.

4. Can a transformation matrix on tensors be applied to any type of tensor?

Yes, a transformation matrix on tensors can be applied to any type of tensor, including scalars, vectors, matrices, and higher-order tensors. However, the dimensions of the matrix must match the dimensions of the tensor in order for the multiplication to be possible.

5. Are there any limitations to using a transformation matrix on tensors?

One limitation of using a transformation matrix on tensors is that it can only represent linear transformations, meaning that it cannot handle non-linear operations such as multiplicative interactions. Additionally, the size and complexity of the tensors can also impact the efficiency and accuracy of the transformation, and may require the use of specialized techniques.

Similar threads

  • Linear and Abstract Algebra
Replies
6
Views
880
  • Linear and Abstract Algebra
Replies
4
Views
1K
  • Linear and Abstract Algebra
Replies
1
Views
823
  • Linear and Abstract Algebra
Replies
5
Views
10K
  • Linear and Abstract Algebra
Replies
2
Views
2K
  • Calculus and Beyond Homework Help
Replies
26
Views
2K
  • Linear and Abstract Algebra
Replies
4
Views
4K
  • Differential Geometry
Replies
9
Views
418
Replies
40
Views
2K
  • Linear and Abstract Algebra
2
Replies
59
Views
8K
Back
Top