Transformation Rules For A General Tensor M

In summary, tensors can be represented using algebraic notation as $$M_{c_1,~c_2,~c_3,...~c_g}^{v_1,~v_2,~v_3,...~v_h}$$ and can be composed of basis vectors and basis covectors, represented using $$\vec{e}_{subscript}$$ and $$\epsilon^{superscript}$$, respectively. The transformation rules for tensors involve taking the product of the components of the tensor with its basis vectors and covectors. This can be represented as $$M = M_{c_1,~c_2,~c_3,...~c_g}^{v_1,~v_
  • #1
Vanilla Gorilla
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TL;DR Summary
Is it correct to say that a bunch of tensor products of all the basis vectors and covectors composing a general tensor M, multiplied by the components of M, lead to the transformation rule for M?
So, I've been watching eigenchris's video series "Tensors for Beginners" on YouTube. I am currently on video 14. I am a complete beginner and just want some clarification on if I'm truly understanding the material.
Basically, is everything below this correct?

In summary of the derivation of the transformation rules for a general tensor $$M$$, using algebraic notation, we just write the product of the components of the tensor $$M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$, with its basis vectors and basis covectors, where $$\vec {e}_{subscript}$$ represents basis vectors, and $$\epsilon^{superscript}$$ represents basis covectors.
This looks like $$\large {M = M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h} \otimes_{n=1}^h {\vec {e}_{n_1}} \otimes_{p=1}^g {\epsilon^{p_1}}}$$
Where $$\large {\otimes_{n=1}^h {\vec {e}_{n_1}} \otimes_{p=1}^g {\epsilon^{p_1}}}$$ is basically just representative of a bunch of tensor products of all the basis vectors and covectors which compose $$M$$ in reference to the definition of tensors as
6834xQzmX8NTLHPe6phiNmc6375MDXsR4tsTRcctcvDKy1P2Mw.png

(Note that I use the notation $$\otimes_{n=1}^h$$ to denote a series of tensor products from $$n=1$$ to $$h$$)
Then, we use the covector and vector rules given here,
4pqhMVhpzdl82WK7BYYZb3nWjHvGU5XcRULbVCkoyACb7uf2Pw.png

Substitute those into the formula given previously,
$$\large {M = M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h} \otimes_{n=1}^h {\vec {e}_{n_1}} \otimes_{p=1}^g {\epsilon^{p_1}}}$$
Use linearity to bring the forward and/or backward transforms next to $$M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$, with the backward transforms in front of $$M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$, and the forward transforms behind it. I note this detail because I'm 99% sure we don't have commutativity with tensors in general; please correct me if I'm wrong about that, though!
All these transforms in conjunction act on $$M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$ to give $$\tilde {M}_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$
The same general process can be done in reverse to convert $$\tilde {M}_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$ to give $$M_{c_1, ~ c_2, ~ c_3,... ~ c_g}^{v_1, ~ v_2, ~ v_3,... ~ v_h}$$

P.S., I'm not always great at articulating my thoughts, so my apologies if this question isn't clear.
 
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  • #2


First of all, great job on taking the initiative to learn about tensors and seeking clarification to ensure your understanding is correct. It's always important to have a solid understanding of the fundamentals before moving on to more advanced concepts.

From what I can see, your understanding of the transformation rules for a general tensor is correct. The notation you have used is also correct and in line with the standard notation for tensors.

To address your concern about commutativity, you are correct that tensors in general do not commute. This is because tensors can represent quantities that have both magnitude and direction, such as forces and velocities. Therefore, the order in which they are multiplied can affect the final result.

However, in the context of tensor transformation rules, we are dealing with coordinate transformations, which are linear operations. And linear operations do commute, which is why we can use linearity to bring the forward and backward transforms next to the tensor, as you have correctly noted.

Overall, your understanding of the material seems to be on the right track. Keep watching the videos and practicing with examples to solidify your understanding. And don't hesitate to ask for clarification if you come across any other doubts or questions. Good luck!
 

1. What is a general tensor M?

A general tensor M is a mathematical object that represents a relationship between different quantities in a multi-dimensional space. It is commonly used in physics and engineering to describe the properties of a system.

2. What are transformation rules for a general tensor M?

The transformation rules for a general tensor M describe how the components of the tensor change when the coordinate system used to describe the space is transformed. These rules are important in understanding how the tensor behaves under different coordinate systems.

3. How do transformation rules for a general tensor M differ from other transformation rules?

The transformation rules for a general tensor M are different from other transformation rules because they take into account the multi-dimensional nature of the tensor. This means that the components of the tensor may change differently depending on the direction of the transformation.

4. Why are transformation rules for a general tensor M important?

Transformation rules for a general tensor M are important because they allow us to understand how the tensor behaves under different coordinate systems. This is crucial in many fields of science and engineering, as it allows us to accurately describe and predict the behavior of systems in different contexts.

5. How can I apply transformation rules for a general tensor M in my research?

If you are working in a field that involves multi-dimensional systems, such as physics or engineering, understanding and applying transformation rules for a general tensor M can greatly enhance your research. By using these rules, you can accurately describe and analyze the behavior of complex systems in different coordinate systems.

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