The Relationship between Covariant Derivative and Riemann Curvature Tensor
📂Geometry The Relationship between Covariant Derivative and Riemann Curvature Tensor Theorem Let f : A ⊂ R 2 → M f : A \subset \mathbb{R}^{2} \to M f : A ⊂ R 2 → M be a parametrized surface . Let ( s , t ) (s, t) ( s , t ) be the standard coordinates of R 2 \mathbb{R}^{2} R 2 . Let V = V ( s , t ) V = V(s,t) V = V ( s , t ) be a vector field following f f f . At each point ( s , t ) (s, t) ( s , t ) , the following holds:
D ∂ t D ∂ s V − D ∂ s D ∂ t V = R ( ∂ f ∂ s , ∂ f ∂ t ) V
\dfrac{D }{\partial t} \dfrac{D }{\partial s}V - \dfrac{D }{\partial s} \dfrac{D }{\partial t}V = R(\dfrac{\partial f}{\partial s}, \dfrac{\partial f}{\partial t})V
∂ t D ∂ s D V − ∂ s D ∂ t D V = R ( ∂ s ∂ f , ∂ t ∂ f ) V
Description Proof Choose a coordinate system ( U , x ) (U, \mathbf{x}) ( U , x ) at point p p p on the manifold M M M of differentiable manifolds . Let { X i = ∂ ∂ x i } \left\{ X_{i} = \dfrac{\partial }{\partial x_{i}} \right\} { X i = ∂ x i ∂ } be the basis of the tangent space T p M T_{p}M T p M . And let’s say V = ∑ i v i X i , v i = v i ( s , t ) V = \sum_{i}v^{i}X_{i}, v^{i} = v^{i}(s, t) V = ∑ i v i X i , v i = v i ( s , t ) . Then, by the properties of the covariant derivative ,
D ∂ s V = D ∂ s ( ∑ i v i X i ) = ∑ i v i D ∂ s X i + ∑ i ∂ v i ∂ s X i
\dfrac{D }{\partial s}V = \dfrac{D }{\partial s}(\sum_{i} v^{i}X_{i}) = \sum_{i}v^{i}\dfrac{D }{\partial s}X_{i} + \sum_{i} \dfrac{\partial v^{i}}{\partial s}X_{i}
∂ s D V = ∂ s D ( i ∑ v i X i ) = i ∑ v i ∂ s D X i + i ∑ ∂ s ∂ v i X i
Applying D ∂ t \dfrac{D }{\partial t} ∂ t D again to this gives the following.
D ∂ t ( D ∂ s V ) = D ∂ t ( ∑ i v i D ∂ s X i + ∑ i ∂ v i ∂ s X i ) = ∑ i v i D ∂ t D ∂ s X i + ∑ i ∂ v i ∂ t D ∂ s X i + ∑ i ∂ v i ∂ s D ∂ t X i + ∑ i ∂ 2 v i ∂ t ∂ s X i
\begin{align*}
\dfrac{D }{\partial t}\left( \dfrac{D }{\partial s}V \right) &= \dfrac{D }{\partial t}\left( \sum_{i}v^{i}\dfrac{D }{\partial s}X_{i} + \sum_{i} \dfrac{\partial v^{i}}{\partial s}X_{i} \right) \\
&= \sum_{i}v^{i}\dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} + \sum_{i}\dfrac{\partial v^{i}}{\partial t}\dfrac{D }{\partial s}X_{i} + \sum_{i} \dfrac{\partial v^{i}}{\partial s}\dfrac{D }{\partial t}X_{i} + \sum_{i} \dfrac{\partial^{2} v^{i}}{\partial t\partial s}X_{i}
\end{align*}
∂ t D ( ∂ s D V ) = ∂ t D ( i ∑ v i ∂ s D X i + i ∑ ∂ s ∂ v i X i ) = i ∑ v i ∂ t D ∂ s D X i + i ∑ ∂ t ∂ v i ∂ s D X i + i ∑ ∂ s ∂ v i ∂ t D X i + i ∑ ∂ t ∂ s ∂ 2 v i X i
Calculating D ∂ s ( D ∂ t V ) \dfrac{D }{\partial s}\left( \dfrac{D }{\partial t}V \right) ∂ s D ( ∂ t D V ) in the same way and subtracting one from the other, it can be seen that the last three terms in the above equation cancel out each other. Therefore,
D ∂ t D ∂ s V − D ∂ s D ∂ t V = ∑ i ( v i D ∂ t D ∂ s X i − v i D ∂ s D ∂ t X i ) = ∑ i v i ( D ∂ t D ∂ s X i − D ∂ s D ∂ t X i )
\begin{equation}
\begin{aligned}
\dfrac{D }{\partial t} \dfrac{D }{\partial s}V - \dfrac{D }{\partial s} \dfrac{D }{\partial t}V &= \sum_{i}\left( v^{i}\dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} - v^{i}\dfrac{D }{\partial s}\dfrac{D }{\partial t}X_{i} \right) \\
&= \sum_{i}v^{i}\left( \dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} - \dfrac{D }{\partial s}\dfrac{D }{\partial t}X_{i} \right)
\end{aligned}
\label{1}
\end{equation}
∂ t D ∂ s D V − ∂ s D ∂ t D V = i ∑ ( v i ∂ t D ∂ s D X i − v i ∂ s D ∂ t D X i ) = i ∑ v i ( ∂ t D ∂ s D X i − ∂ s D ∂ t D X i )
Now, let’s say p = f ( s , t ) = x ( x 1 ( s , t ) , … , x n ( s , t ) ) p = f(s,t) = \mathbf{x}(x_{1}(s,t), \dots, x_{n}(s,t)) p = f ( s , t ) = x ( x 1 ( s , t ) , … , x n ( s , t )) . Calculating ∂ f ∂ s \dfrac{\partial f}{\partial s} ∂ s ∂ f ,
∂ f ∂ s : = d f ( ∂ ∂ s ) = [ ∂ x 1 ∂ s ∂ x 1 ∂ t ⋮ ⋮ ∂ x n ∂ s ∂ x n ∂ t ] [ 1 0 ] = [ ∂ x 1 ∂ s ⋮ ∂ x n ∂ s ] = ∂ x j ∂ s X j
\begin{align*}
\dfrac{\partial f}{\partial s} &:= df(\dfrac{\partial }{\partial s}) \\
&= \begin{bmatrix} \dfrac{\partial x_{1}}{\partial s} & \dfrac{\partial x_{1}}{\partial t} \\ \vdots & \vdots \\ \dfrac{\partial x_{n}}{\partial s} & \dfrac{\partial x_{n}}{\partial t}\end{bmatrix} \begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} \dfrac{\partial x_{1}}{\partial s} \\ \vdots \\ \dfrac{\partial x_{n}}{\partial s} \end{bmatrix} = \dfrac{\partial x_{j}}{\partial s}X_{j}
\end{align*}
∂ s ∂ f := df ( ∂ s ∂ ) = ∂ s ∂ x 1 ⋮ ∂ s ∂ x n ∂ t ∂ x 1 ⋮ ∂ t ∂ x n [ 1 0 ] = ∂ s ∂ x 1 ⋮ ∂ s ∂ x n = ∂ s ∂ x j X j
Similarly, ∂ f ∂ t = ∂ x k ∂ t X k \dfrac{\partial f}{\partial t} = \dfrac{\partial x_{k}}{\partial t}X_{k} ∂ t ∂ f = ∂ t ∂ x k X k . Now, calculating D ∂ t D ∂ s X i \dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} ∂ t D ∂ s D X i ,
D ∂ s X i = ∇ ∂ f / ∂ s X i = ∇ ( ∂ x j / ∂ s ) X j X i = ∂ x j ∂ s ∇ X j X i
\dfrac{D }{\partial s}X_{i} = \nabla_{\partial f/\partial s}X_{i} = \nabla_{(\partial x_{j}/\partial s)X_{j}}X_{i} = \dfrac{\partial x_{j}}{\partial s}\nabla_{X_{j}}X_{i}
∂ s D X i = ∇ ∂ f / ∂ s X i = ∇ ( ∂ x j / ∂ s ) X j X i = ∂ s ∂ x j ∇ X j X i
And
D ∂ t D ∂ s X i = D ∂ t ( ∂ x j ∂ s ∇ X j X i ) = ∂ 2 x j ∂ t ∂ s ∇ X j X i + ∂ x j ∂ s ∇ ∂ f / ∂ t ( ∇ X j X i ) = ∂ 2 x j ∂ t ∂ s ∇ X j X i + ∂ x j ∂ s ∇ ( ∂ x k / ∂ t ) X k ( ∇ X j X i ) = ∂ 2 x j ∂ t ∂ s ∇ X j X i + ∂ x j ∂ s ∂ x k ∂ t ∇ X k ∇ X j X i
\begin{align*}
\dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} &= \dfrac{D }{\partial t}\left( \dfrac{\partial x_{j}}{\partial s}\nabla_{X_{j}}X_{i} \right) \\
&= \dfrac{\partial^{2} x_{j}}{\partial t\partial s}\nabla_{X_{j}}X_{i} + \dfrac{\partial x_{j}}{\partial s}\nabla_{\partial f / \partial t}(\nabla_{X_{j}}X_{i}) \\
&= \dfrac{\partial^{2} x_{j}}{\partial t\partial s}\nabla_{X_{j}}X_{i} + \dfrac{\partial x_{j}}{\partial s}\nabla_{(\partial x_{k} / \partial t})X_{k}(\nabla_{X_{j}}X_{i}) \\
&= \dfrac{\partial^{2} x_{j}}{\partial t\partial s}\nabla_{X_{j}}X_{i} + \dfrac{\partial x_{j}}{\partial s}\dfrac{\partial x_{k}}{\partial t}\nabla_{X_{k}}\nabla_{X_{j}}X_{i}
\end{align*}
∂ t D ∂ s D X i = ∂ t D ( ∂ s ∂ x j ∇ X j X i ) = ∂ t ∂ s ∂ 2 x j ∇ X j X i + ∂ s ∂ x j ∇ ∂ f / ∂ t ( ∇ X j X i ) = ∂ t ∂ s ∂ 2 x j ∇ X j X i + ∂ s ∂ x j ∇ ( ∂ x k / ∂ t ) X k ( ∇ X j X i ) = ∂ t ∂ s ∂ 2 x j ∇ X j X i + ∂ s ∂ x j ∂ t ∂ x k ∇ X k ∇ X j X i
Therefore, the following is obtained.
D ∂ t D ∂ s X i − D ∂ s D ∂ t X i = ∂ x j ∂ s ∂ x k ∂ t ( ∇ X k ∇ X j X i − ∇ X j ∇ X k X i )
\dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} - \dfrac{D }{\partial s}\dfrac{D }{\partial t}X_{i} = \dfrac{\partial x_{j}}{\partial s}\dfrac{\partial x_{k}}{\partial t}\left( \nabla_{X_{k}}\nabla_{X_{j}}X_{i} - \nabla_{X_{j}}\nabla_{X_{k}}X_{i}\right)
∂ t D ∂ s D X i − ∂ s D ∂ t D X i = ∂ s ∂ x j ∂ t ∂ x k ( ∇ X k ∇ X j X i − ∇ X j ∇ X k X i )
However, since [ X i , X j ] = 0 [X_{i}, X_{j}]=0 [ X i , X j ] = 0 , the Riemann curvature is
R ( X j , X k ) X i = ∇ X k ∇ X j X i − ∇ X j ∇ X k X i + ∇ [ X j , X k ] X i = ∇ X k ∇ X j X i − ∇ X j ∇ X k X i
R(X_{j}, X_{k})X_{i} = \nabla_{X_{k}}\nabla_{X_{j}}X_{i} - \nabla_{X_{j}}\nabla_{X_{k}}X_{i} + \nabla_{[X_{j}, X_{k}]}X_{i} = \nabla_{X_{k}}\nabla_{X_{j}}X_{i} - \nabla_{X_{j}}\nabla_{X_{k}}X_{i}
R ( X j , X k ) X i = ∇ X k ∇ X j X i − ∇ X j ∇ X k X i + ∇ [ X j , X k ] X i = ∇ X k ∇ X j X i − ∇ X j ∇ X k X i
And the above equation is,
D ∂ t D ∂ s X i − D ∂ s D ∂ t X i = ∂ x j ∂ s ∂ x k ∂ t R ( X j , X k ) X i
\dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} - \dfrac{D }{\partial s}\dfrac{D }{\partial t}X_{i} = \dfrac{\partial x_{j}}{\partial s}\dfrac{\partial x_{k}}{\partial t}R(X_{j}, X_{k})X_{i}
∂ t D ∂ s D X i − ∂ s D ∂ t D X i = ∂ s ∂ x j ∂ t ∂ x k R ( X j , X k ) X i
By substituting this into ( 1 ) \eqref{1} ( 1 ) , since R R R is linear,
D ∂ t D ∂ s V − D ∂ s D ∂ t V = ∑ i v i ( D ∂ t D ∂ s X i − D ∂ s D ∂ t X i ) = ∑ i v i ∂ x j ∂ s ∂ x k ∂ t R ( X j , X k ) X i = ∑ i R ( ∂ x j ∂ s X j , ∂ x k ∂ t X k ) v i X i = ∑ i R ( ∂ f ∂ s , ∂ f ∂ t ) V
\begin{align*}
\dfrac{D }{\partial t} \dfrac{D }{\partial s}V - \dfrac{D }{\partial s} \dfrac{D }{\partial t}V
&= \sum_{i}v^{i}\left( \dfrac{D }{\partial t}\dfrac{D }{\partial s}X_{i} - \dfrac{D }{\partial s}\dfrac{D }{\partial t}X_{i} \right) \\
&= \sum_{i}v^{i}\dfrac{\partial x_{j}}{\partial s}\dfrac{\partial x_{k}}{\partial t}R(X_{j}, X_{k})X_{i} \\
&= \sum_{i}R(\dfrac{\partial x_{j}}{\partial s}X_{j}, \dfrac{\partial x_{k}}{\partial t}X_{k})v^{i}X_{i} \\
&= \sum_{i}R(\dfrac{\partial f}{\partial s}, \dfrac{\partial f}{\partial t})V
\end{align*}
∂ t D ∂ s D V − ∂ s D ∂ t D V = i ∑ v i ( ∂ t D ∂ s D X i − ∂ s D ∂ t D X i ) = i ∑ v i ∂ s ∂ x j ∂ t ∂ x k R ( X j , X k ) X i = i ∑ R ( ∂ s ∂ x j X j , ∂ t ∂ x k X k ) v i X i = i ∑ R ( ∂ s ∂ f , ∂ t ∂ f ) V
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