Uncovering the Derivative of a Tensor: Understanding its Equations and Origins

In summary, the first equal sign holds due to the chain rule and transformation properties of vector components. The second equal sign is correct and can be derived by applying the product rule. The first term only has one derivative because it arises from the derivative acting on the vector component and the derivatives outside cancelling, another instance of the chain rule. The cancellation occurs because of the uniqueness of the dependence of the vector components, as they are the inverse of one another.
  • #1
mk9898
109
9
How/why does the first equal sign hold? Where does each derivative come from:
Bildschirmfoto 2019-04-05 um 18.35.06.png
 
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  • #2
Its just the chain rule and the transformation properties of the vector components.
 
  • #3
Ahh they just switched the two chain rule derivatives. Got it. Thank you. Another question: is the second equal sign correct? Why does the first term only have one derivative?
 
  • #4
The second equal sign is correct. Apply the product rule. The first term arises from the derivative acting on the vector component and the derivatives outside cancelling (another instance of the chain rule).
 
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  • #5
I'm not seeing how they cancel since they are operators.

Here is what I see:

##\frac{\partial \tilde x^k}{\partial x^i}\frac{\partial^2 x^i}{\partial \tilde x^k \partial \tilde x^l}##But since they are operators, I don't see how the two can cancel each other out and the fact it's the derivative squared so we cannot move one of derivatives to the right and let them somehow cancel out like so:

##\frac{\partial \tilde x^k}{\partial x^i}\frac{\partial x^i \partial}{\partial \tilde x^k \partial \tilde x^l}##

Is it due to the uniqueness of the dependence of the vector components that they are the inverse of one another?
 
  • #6
mk9898 said:
I'm not seeing how they cancel since they are operators.
That is the second term, not the first term...
 
  • #7
Ah got it. Yea it's just then the Kronecker delta and the k's become l's. Got it thanks!
 
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1. What is a tensor and why is it important in science?

A tensor is a mathematical object that represents a physical quantity with multiple components, each of which can vary in different directions. It is important in science because it allows us to describe and analyze complex systems with multiple variables, such as fluid dynamics, electromagnetism, and general relativity.

2. How is the derivative of a tensor calculated?

The derivative of a tensor is calculated using the same rules as the derivative of a scalar. However, since a tensor has multiple components, the derivative will also have multiple components, resulting in a tensor of a higher rank. This can be done using the chain rule and the product rule, as well as the rules for differentiating trigonometric and exponential functions.

3. What are the origins of the concept of a tensor?

The concept of a tensor was first introduced by mathematician Gregorio Ricci-Curbastro in the late 19th century as a way to generalize the concept of a vector. It was further developed by mathematician Tullio Levi-Civita and physicist Albert Einstein in the early 20th century as a key tool in their formulation of the theory of general relativity.

4. How is a tensor represented mathematically?

A tensor can be represented mathematically as a multi-dimensional array of numbers, with each number representing a component of the tensor in a specific direction. The number of indices in the array corresponds to the rank of the tensor, and the dimensions of the array correspond to the number of components in each direction.

5. What are some real-world applications of tensors?

Tensors have many real-world applications in various fields of science and engineering. In physics, they are used to describe the stress and strain of materials, as well as the curvature of space-time in general relativity. In engineering, they are used in the design of structures and machines, as well as in computer vision and image processing. They are also used in machine learning and data analysis, such as in the field of deep learning for image and speech recognition.

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