Question about tensor derivative

In summary, the second term in the last equation is used to find the velocity of the particle given the coordinates and the Lagrangian.
  • #1
lync4495
2
0
Hello,

I'm beginning to learn GR and ran across the following in the beginning of Stephani's book.

In deriving the equations of motion for a force-free particle, we have the Lagrangian:
[tex]L = \frac{m}{2} g_{\alpha \beta} \dot{x}^\alpha \dot{x}^\beta[/tex]

To construct the equations of motion we need
[tex]\frac{\partial L}{\partial \dot{x}^\nu} = m g_{\alpha \nu} \dot{x}^\alpha[/tex]

and

[tex]\frac{\partial L}{\partial x^\nu} = L_{,\nu} = \frac{m}{2} g_{\alpha \beta, \nu} \dot{x}^\alpha \dot{x}^\beta[/tex]

Then from the Lagrange equations we get

[tex]g_{\alpha \nu }\ddot{x}^\alpha + g_{\alpha \nu, \beta} \dot{x}^\alpha \dot{x}^\beta - \frac{1}{2}g_{\alpha \beta, \nu}\dot{x}^\alpha \dot{x}^\beta =0[/tex]

I don't understand where the second term in the last equation comes from.
 
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  • #2
lync4495 said:
Hello,

I'm beginning to learn GR and ran across the following in the beginning of Stephani's book.

In deriving the equations of motion for a force-free particle, we have the Lagrangian:
[tex]L = \frac{m}{2} g_{\alpha \beta} \dot{x}^\alpha \dot{x}^\beta[/tex]

To construct the equations of motion we need
[tex]\frac{\partial L}{\partial \dot{x}^\nu} = m g_{\alpha \nu} \dot{x}^\alpha[/tex]

and

[tex]\frac{\partial L}{\partial x^\nu} = L_{,\nu} = \frac{m}{2} g_{\alpha \beta, \nu} \dot{x}^\alpha \dot{x}^\beta[/tex]

Then from the Lagrange equations we get

[tex]g_{\alpha \nu }\ddot{x}^\alpha + g_{\alpha \nu, \beta} \dot{x}^\alpha \dot{x}^\beta - \frac{1}{2}g_{\alpha \beta, \nu}\dot{x}^\alpha \dot{x}^\beta =0[/tex]

I don't understand where the second term in the last equation comes from.

It comes from the product and chain rules:

[tex]\frac{d}{d \tau} \left( \frac{\partial L}{\partial \dot{x}^\nu} \right) = m \frac{d}{d \tau} \left( g_{\alpha \nu} \dot{x}^\alpha \right) = m \frac{d}{d \tau} \left( g_{\alpha \nu} \right) \dot{x}^\alpha + m g_{\alpha \nu} \frac{d}{d \tau} \left( \dot{x}^\alpha \right). [/tex]

In the second-last term,

[tex]\frac{d}{d \tau} = \frac{d x^\beta}{d \tau}\frac{\partial}{\partial x^\beta}[/tex]

is used.
 
  • #3
Oh ok. Thank you.

I wasn't realizing the metric tensor was a function of time. Now that I think about it, it makes sense that it is.
 

FAQ: Question about tensor derivative

1. What is a tensor derivative?

A tensor derivative is a mathematical operation that calculates the rate of change of a tensor with respect to another tensor or a scalar. It is commonly used in fields such as physics, engineering, and machine learning to model complex systems.

2. How is a tensor derivative different from a regular derivative?

A tensor derivative takes into account the multidimensional nature of tensors, whereas a regular derivative only considers one-dimensional data. This means that a tensor derivative can capture more information and provide a more accurate representation of a system.

3. What are the applications of tensor derivatives?

Tensor derivatives have a wide range of applications in various fields, including mechanics, fluid dynamics, computer vision, and neural networks. They are used to model complex systems and make predictions about their behavior.

4. How is a tensor derivative calculated?

A tensor derivative is calculated using the chain rule, which involves taking partial derivatives of the tensor with respect to each of its components. This process can be complicated for higher-order tensors, but there are various methods and algorithms that can be used to simplify the calculation.

5. What are some common challenges when working with tensor derivatives?

One of the main challenges of working with tensor derivatives is the complexity of the calculations, especially for higher-order tensors. Another challenge is choosing the appropriate method or algorithm for a particular problem, as there are many different approaches to calculating tensor derivatives. Additionally, the interpretation of the results can be difficult, as tensors can represent complex systems with many variables.

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