A Calculating Lie Derivatives for Tensors & Vectors

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The discussion focuses on calculating Lie derivatives for various tensor types, including scalars, vectors, and rank two tensors. The user has successfully defined the covariant derivative for scalar functions, vectors, and rank two tensors, and is seeking to express the Lie derivatives for these entities. Key formulas for the Lie derivatives of vectors and tensors are provided, emphasizing their differences from covariant derivatives. The conversation also clarifies that the Lie derivative acts on functions as a vector field and is applicable in any dimension, including 3D space. Overall, the thread serves as a resource for understanding the relationship between covariant and Lie derivatives in tensor calculus.
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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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
$$
\mathcal L_X T^{a_1a_2\ldots}_{b_1b_2\ldots} = X^c \partial_c T^{a_1a_2\ldots}_{b_1b_2\ldots} - T^{ca_2\ldots}_{b_1b_2\ldots} \partial_c X^{a_1} - T^{a_1c\ldots}_{b_1b_2\ldots} \partial_c X^{a_2} - \ldots + T^{a_1a_2\ldots}_{cb_2\ldots} \partial_{b_1} X^c + T^{a_1a_2\ldots}_{b_1c\ldots} \partial_{b_2} X^c + \ldots
$$
 
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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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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Likes vanhees71 and Arman777
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)##.
 
In all of these calculations ##X## is just a vector right ?
 
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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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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thanks
 
  • #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