Calculating Lie Derivatives for Tensors & Vectors

In summary, the Lie derivative is defined as a transformation that satisfies Leibniz' rule and does not rely on any underlying structure. It is fundamentally different from the covariant derivative defined by a connection. The general structure of the Lie derivative of a tensor in terms of components is given by the equation $\mathcal{L}_X T^{a_1a_2\ldots}_{b_1b_2\ldots} = X^c \partial_c T^{a_1a_2\ldots}_{b_1b_2\ldots} - T^{ca_2\ldots}_{b_1b_2\ldots} \partial_c X^{a_1} -
  • #1
Arman777
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I am writing a code to calculate the Lie Derivatives, and so far, I have defined the Covariant derivative

1) for scalar function;

$$\nabla_a\phi \equiv \partial_a\phi~~(1)$$

2) for vectors;

$$\nabla_bV^a = \partial_bV^a + \Gamma^a_{bc}V^c~~(2)$$
$$\nabla_cV_a = \partial_cV_a - \Gamma^b_{ca}V_b~~(3)$$

3) for rank two tensors;

$$\nabla_cT^{ab} = \partial_cT^{ab} + \Gamma^a_{cd}T^{db} + \Gamma^b_{cd}T^{ad}~~(4)$$
$$\nabla_cT^a_b = \partial_cT^a_b + \Gamma^a_{cd}T^d_b - \Gamma^d_{cb}T^a_d~~(5)$$
$$\nabla_cT_{ab} = \partial_cT_{ab} - \Gamma^d_{ca}T_{db} - \Gamma^d_{cb}T_{ad}~~(6)$$

Similarly, I want to obtain a Lie Derivative of a scalar function, a vector (covariant and contravariant), and a tensor with rank 2.

From some research, I have found that.

1) for scalar function;

$$L_X\phi = X^{a}\partial_a\phi$$

So my questions is how can I write

$$L_XV^a, L_XV_a, L_XT^{ab}, L_XT^a_b, L_XT_{ab}$$ in terms of Eqns. ##(2), (3), (4), (5), (6)##, If possible. If it's not possible how can I write them in general.
 
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  • #2
The Lie derivative is fundamentally different from the covariant derivative defined by a connection and it does not rely on any underlying structure (such as a connection). The general structure of the Lie derivative of a tensor in terms of components is
$$
\mathcal L_X T^{a_1a_2\ldots}_{b_1b_2\ldots} = X^c \partial_c T^{a_1a_2\ldots}_{b_1b_2\ldots} - T^{ca_2\ldots}_{b_1b_2\ldots} \partial_c X^{a_1} - T^{a_1c\ldots}_{b_1b_2\ldots} \partial_c X^{a_2} - \ldots + T^{a_1a_2\ldots}_{cb_2\ldots} \partial_{b_1} X^c + T^{a_1a_2\ldots}_{b_1c\ldots} \partial_{b_2} X^c + \ldots
$$
 
  • #3
I have found these,
$$L_XV^a = X^c\partial_cV^a - V^c\partial_cX^a$$
$$L_XV_b = X^c\partial_cV_b + V_c\partial_bX^c$$
$$L_XT^{ab} = X^c\partial_cT^{ab} - T^{cb}\partial_cX^a - T^{ac}\partial_cX^b$$
$$L_XT^a_b = X^c\partial_cT^a_b - T^c_b\partial_cX^a + T^a_c\partial_bX^c$$
$$L_XT_{ab} = X^c\partial_cT_{ab} + T_{cb}\partial_aX^c + T_{ac}\partial_bX^c$$

Can somone check please
 
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  • #4
For sake of an example, consider a ##(1,1)## tensor ##\mathbf{T}##. The Lie derivative ##L_{\mathbf{X}}## satisfies Leibniz' rule, hence\begin{align*}
L_{\mathbf{X}} ( \mathbf{T} \otimes \mathbf{e}^a \otimes \mathbf{e}_b ) = L_{\mathbf{X}} \mathbf{T} \otimes \mathbf{e}^a \otimes \mathbf{e}_b + \mathbf{T} \otimes L_{\mathbf{X}} \mathbf{e}^a \otimes \mathbf{e}_b + \mathbf{T} \otimes \mathbf{e}^a \otimes L_{\mathbf{X}} \mathbf{e}_b
\end{align*}for arbitrary basis vectors ##\mathbf{e}^a## and ##\mathbf{e}_b##. Now contract over all four positions; recall that ##L_{\mathbf{X}}## preserves contractions.\begin{align*}

(\dagger) \quad L_{\mathbf{X}} {T^a}_b &= {(L_{\mathbf{X}} \mathbf{T})^a}_b + {T^{i}}_j \langle L_{\mathbf{X}} \mathbf{e}^a, \mathbf{e}_i \rangle \langle \mathbf{e}^j, \mathbf{e}_b \rangle + {T^{i}}_j \langle \mathbf{e}^a, \mathbf{e}_i \rangle \langle \mathbf{e}^j, L_{\mathbf{X}} \mathbf{e}_b \rangle \\

&= {(L_{\mathbf{X}} \mathbf{T})^a}_b + {T^{i}}_b \langle L_{\mathbf{X}} \mathbf{e}^a, \mathbf{e}_i \rangle + {T^{a}}_j \langle \mathbf{e}^j, L_{\mathbf{X}} \mathbf{e}_b \rangle

\end{align*}By definition the Lie derivative acts on functions as ##L_{\mathbf{X}} f = \mathbf{X}f## hence in coordinate basis ##L_{\mathbf{X}} {T^a}_b = X^i \dfrac{\partial {T^a}_b}{\partial x^i}##. Furthermore, for vectors ##\mathbf{Y} \in T_p## and covectors ##\boldsymbol{\eta} \in T^*_p## we have*\begin{align*}
(L_{\mathbf{X}} \mathbf{Y})^i &= \dfrac{\partial Y^i}{\partial x^j} X^j - \dfrac{\partial X^i}{\partial x^j} Y^j \\

(L_{\mathbf{X}} \boldsymbol{\eta})_i &= \dfrac{\partial \eta_i}{\partial x^j} X^j + \dfrac{\partial X^j}{\partial x^i} \eta_j
\end{align*}which imply that for a coordinate basis ##\left(L_{\mathbf{X}} \dfrac{\partial}{\partial x^i} \right)^j = -\dfrac{\partial X^j}{\partial x^i}## and ##(L_{\mathbf{X}} dx^i)_j = \dfrac{\partial X^i}{\partial x^j}##. Returning to equation ##(\dagger)##,\begin{align*}
X^i \dfrac{\partial {T^a}_b}{\partial x^i} &= {(L_{\mathbf{X}} \mathbf{T})^a}_b + {T^{i}}_b \dfrac{\partial X^a}{\partial x^i} - {T^{a}}_j \dfrac{\partial X^j}{\partial x^b}
\end{align*}which is better re-written as \begin{align*}
{(L_{\mathbf{X}} \mathbf{T})^a}_b &= X^i \dfrac{\partial {T^a}_b}{\partial x^i} - {T^{i}}_b \dfrac{\partial X^a}{\partial x^i} + {T^{a}}_j \dfrac{\partial X^j}{\partial x^b} \\
\end{align*}The mnemonic is "minus the upper indices & plus the lower indices".

*the latter of these identities is deduced from the former. The former comes from the definition ##L_{\mathbf{X}} \mathbf{S} |_p = \lim_{t \rightarrow 0} \frac{1}{t} \left( \mathbf{S} |_p - \phi_{t*} \mathbf{S} |_p \right)##.
 
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  • #5
Thanks a lot. These were helpful
ergospherical said:
For sake of an example, consider a ##(1,1)## tensor ##\mathbf{T}##. The Lie derivative ##L_{\mathbf{X}}## satisfies Leibniz' rule, hence\begin{align*}
L_{\mathbf{X}} ( \mathbf{T} \otimes \mathbf{e}^a \otimes \mathbf{e}_b ) = L_{\mathbf{X}} \mathbf{T} \otimes \mathbf{e}^a \otimes \mathbf{e}_b + \mathbf{T} \otimes L_{\mathbf{X}} \mathbf{e}^a \otimes \mathbf{e}_b + \mathbf{T} \otimes \mathbf{e}^a \otimes L_{\mathbf{X}} \mathbf{e}_b
\end{align*}for arbitrary basis vectors ##\mathbf{e}^a## and ##\mathbf{e}_b##. Now contract over all four positions; recall that ##L_{\mathbf{X}}## preserves contractions.\begin{align*}

(\dagger) \quad L_{\mathbf{X}} {T^a}_b &= {(L_{\mathbf{X}} \mathbf{T})^a}_b + {T^{i}}_j \langle L_{\mathbf{X}} \mathbf{e}^a, \mathbf{e}_i \rangle \langle \mathbf{e}^j, \mathbf{e}_b \rangle + {T^{i}}_j \langle \mathbf{e}^a, \mathbf{e}_i \rangle \langle \mathbf{e}^j, L_{\mathbf{X}} \mathbf{e}_b \rangle \\

&= {(L_{\mathbf{X}} \mathbf{T})^a}_b + {T^{i}}_b \langle L_{\mathbf{X}} \mathbf{e}^a, \mathbf{e}_i \rangle + {T^{a}}_j \langle \mathbf{e}^j, L_{\mathbf{X}} \mathbf{e}_b \rangle

\end{align*}By definition the Lie derivative acts on functions as ##L_{\mathbf{X}} f = \mathbf{X}f## hence in coordinate basis ##L_{\mathbf{X}} {T^a}_b = X^i \dfrac{\partial {T^a}_b}{\partial x^i}##. Furthermore, for vectors ##\mathbf{Y} \in T_p## and covectors ##\boldsymbol{\eta} \in T^*_p## we have*\begin{align*}
(L_{\mathbf{X}} \mathbf{Y})^i &= \dfrac{\partial Y^i}{\partial x^j} X^j - \dfrac{\partial X^i}{\partial x^j} Y^j \\

(L_{\mathbf{X}} \boldsymbol{\eta})_i &= \dfrac{\partial \eta_i}{\partial x^j} X^j + \dfrac{\partial X^j}{\partial x^i} \eta_j
\end{align*}which imply that for a coordinate basis ##\left(L_{\mathbf{X}} \dfrac{\partial}{\partial x^i} \right)^j = -\dfrac{\partial X^j}{\partial x^i}## and ##(L_{\mathbf{X}} dx^i)_j = \dfrac{\partial X^i}{\partial x^j}##. Returning to equation ##(\dagger)##,\begin{align*}
X^i \dfrac{\partial {T^a}_b}{\partial x^i} &= {(L_{\mathbf{X}} \mathbf{T})^a}_b + {T^{i}}_b \dfrac{\partial X^a}{\partial x^i} - {T^{a}}_j \dfrac{\partial X^j}{\partial x^b}
\end{align*}which is better re-written as \begin{align*}
{(L_{\mathbf{X}} \mathbf{T})^a}_b &= X^i \dfrac{\partial {T^a}_b}{\partial x^i} - {T^{i}}_b \dfrac{\partial X^a}{\partial x^i} + {T^{a}}_j \dfrac{\partial X^j}{\partial x^b} \\
\end{align*}The mnemonic is "minus the upper indices & plus the lower indices".

*the latter of these identities is deduced from the former. The former comes from the definition ##L_{\mathbf{X}} \mathbf{S} |_p = \lim_{t \rightarrow 0} \frac{1}{t} \left( \mathbf{S} |_p - \phi_{t*} \mathbf{S} |_p \right)##.
 
  • #6
In all of these calculations ##X## is just a vector right ?
 
  • #7
A vector field, technically, since it needs values in a region for ##\partial_i X^j## to make sense, but yes.
 
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  • #8
Yeah, as @Ibix said ##\mathbf{X}## is a vector field on the manifold ##M##, and for each ##p \in M## there is a curve ##\gamma## such that ##\gamma(0) = p## and ##\gamma'(t) = \mathbf{X} |_{\gamma(t)}##. ##\mathbf{X}## generates a flow ##\phi_t## which takes any point ##p## a parameter distance ##t## along the integral curve through ##p##. The difference between a tensor field ##\mathbf{T} |_p## evaluated at ##p## and its push-forward ##\phi_{t*} \mathbf{T}|_p## also evaluated at ##p## ##-## per unit parameter ##t## ##-## is essentially the Lie derivative.
 
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  • #10
As a side question, does the covariant derivative defined in 3D space ?
 
  • #11
It's defined in any dimension, although since 1d manifolds don't have curvature it's unexciting there.
 
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  • #12
Ibix said:
It's defined in any dimension, although since 1d manifolds don't have curvature it's unexciting there.
Okay thanks
 

FAQ: Calculating Lie Derivatives for Tensors & Vectors

What is a Lie derivative?

A Lie derivative is a mathematical operation that describes how a tensor or vector field changes along a given direction. It is used to measure the change of a tensor or vector field with respect to a flow or transformation.

How do you calculate a Lie derivative for tensors and vectors?

The calculation of a Lie derivative involves taking the derivative of a tensor or vector field with respect to a given direction, and then subtracting the result from the original tensor or vector field. This process is repeated for each component of the tensor or vector field.

What is the significance of calculating Lie derivatives?

Calculating Lie derivatives allows us to understand how a tensor or vector field changes under a transformation. This is useful in various fields such as physics, engineering, and mathematics, where understanding the behavior of tensors and vectors is crucial.

Can Lie derivatives be calculated for any type of tensor or vector?

Yes, Lie derivatives can be calculated for any type of tensor or vector field, including scalar fields, vector fields, and higher-order tensors. The calculation method may vary slightly depending on the type of field, but the concept remains the same.

Are there any applications of Lie derivatives in real-world problems?

Yes, Lie derivatives have various applications in real-world problems, such as in fluid mechanics, general relativity, and control theory. They are also used in computer graphics and computer vision to analyze and manipulate images and shapes.

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