Learn Tensor Calculus: Understand Tensor Invariance

In summary, the speaker is confused about tensor invariance when it comes to changes in coordinates. They understand that tensors remain invariant under a change of coordinates, but are unsure if this applies to different inertial reference systems as well. They are seeking clarification on the meaning of "change of coordinates" in terms of tensor invariance. The article referenced provides further explanation and an example of tensor invariance in the context of matrix multiplication.
  • #1
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I am trying to learn tensor calculus, but I must be confused about tensor invariance. I know the definition of a tensor is a number or function that transforms according to certain rules under a change of coordinates. The transformation leaves the number or function invariant if it is a tensor. Here is where I am confused-- when they speak of change of coordinates.
For example:
Let's say there is a vector in an orthogonal x-y coordinate system that has a certain magnitude |v|. Now let's say we obtain a new coordinate system by rotating the original coordinate system counter-clockwise around its origin. I know that with respect to the new coordinate system the vector would still have the same magnitude |v|. Thus, the vector would qualify as a rank1 tensor. This is intuitive and easy to understand.
But, I often read about tensors that are applied with respect to different inertial reference systems. In this case, however, a velocity vector usually is not invariant with respect to two different inertial reference frames. But an acceleration vector is invariant and thus would qualify as a rank 1 tensor.
So, where I am confused has to do with the term "change of coordinates". Is tensor invariance talking about invariance with respect to a change of coordinates as in the first example (a rotated coordinate system) or with respect to the second example (different inertial reference systems.) If someone could clarify this I would appreciate it.
 
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  • #2
It is the same thing as with linear transformations, or vectors. If you change the basis, the representation changes, too, but not the vector or linear transformation. This is obvious in case of real objects and e.g. a rotation of it. If we change the measurement, then we get different numbers although neither object nor rotation has been changed. Now the difficulty is, that we describe object as well as rotation by numbers, and those do change. Thus the invariance is counterintuitive.

Have a look at https://www.physicsforums.com/insights/what-is-a-tensor/
with an example: Strassen's algorithm for matrix multiplication.
 

Related to Learn Tensor Calculus: Understand Tensor Invariance

1. What is tensor calculus?

Tensor calculus is a branch of mathematics that deals with the algebraic and geometric properties of tensors. Tensors are mathematical objects that can represent quantities such as scalars, vectors, and matrices in a higher-dimensional space.

2. Why is tensor calculus important?

Tensor calculus is important because it provides a powerful mathematical framework for understanding and solving problems in physics, engineering, and other fields that involve multidimensional quantities. It allows us to describe and analyze complex systems in a concise and elegant manner.

3. What is tensor invariance?

Tensor invariance refers to the property of tensors that their components remain unchanged under certain transformations, such as rotations or changes in coordinate systems. This property is crucial in physics, where we need to describe physical quantities that are independent of the observer's frame of reference.

4. How can I learn tensor calculus?

There are many resources available for learning tensor calculus, including textbooks, online courses, and tutorials. It is important to have a strong foundation in linear algebra and multivariable calculus before diving into tensor calculus. Practice and working through problems are also essential for understanding the concepts.

5. What are some real-world applications of tensor calculus?

Tensor calculus has many practical applications, especially in the fields of physics and engineering. It is used to describe the stress and strain in materials, model fluid dynamics, and analyze the behavior of electromagnetic fields. It is also used in machine learning and computer graphics for tasks such as image recognition and 3D modeling.

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