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A geometric set $S \subset R^n$ is a set having the property that for each point $p \in S$, there is a vector subspace $T_pS \subset T_p\mathbb R^n$. Moreover, these subspaces should vary smoothly with $p$ and should all have the same dimension. Let $U\subset\mathbb R^k$ be a domain and let $\phi:U\rightarrow\mathbb R^n(k\leq n)$ be a smooth, one-to-one function that is regular for all $p\in U$. A parameterized set $S=\phi(U)$ is defined to be the image of $U$ in $\mathbb R^n$ by $\phi$. The geometric features of the parameterized set $S$ come from “encoding” features of the parameter space $U$ through the function $\phi$.

Then the author (First Steps in Differential Geometry Riemannian, Contact, Symplectic by Andrew McInerney) built intuition like Riemannian metrics, Riemannian Connection and curvature on the geometric set or more specifically the parameterized set.

Proposition 5.1.9. Let $(U, g)$ be a Riemannian space, $\mathcal{X}(U)$ the set of smooth vector fields on $U$, and $\Lambda_1(U)$ the set of smooth one-forms on $U$. Then the map $\gamma: \mathcal{X}(U) \rightarrow \Lambda_1(U)$ given by $\gamma(X)=i(X)$ g for $X \in \mathcal{X}(U)$, i.e., $\gamma(X)$ is the differential one-form such that for any vector field $Y$ on $U$, $$ (\gamma(X))(Y)=g(X, Y), $$ induces a vector space isomorphism $\gamma_p: T_p U \rightarrow T_p^* U$ for all $p \in U$.

Definition 5.3.2. Suppose $X$ and $Y$ are smooth vector fields on a Riemannian space $(U, g)$. Let $\theta_Y=\gamma(Y)$ be the one-form corresponding to the vector field $Y$ under the isomorphism $\gamma$ induced by $g$ defined in Proposition 5.1.9. Construct a new oneform $\theta_{Y, X}$ as follows: $$ \theta_{Y, X}=\frac{1}{2} i(X)\left[\mathcal{L}_Y g+d \theta_Y\right] . $$ The covariant derivative of $Y$ with respect to $X$ (relative to the metric tensor $g$ ), denoted by $\nabla_X Y$, is the vector field $$ \nabla_X Y=\gamma^{-1}\left(\theta_{Y, X}\right) . $$ The assignment $\nabla:(X, Y) \mapsto \nabla_X Y$ is also known as the Riemannian connection corresponding to $g$.

I didn't understand the motivation behind this definition of the covariant derivative, $\nabla_X Y$, as others have explained it as the horizontal (or tangential) component of the directional derivative, $D_X Y$, or as the change of $Y$ in the direction of $X$ using any curve $\gamma$ which goes in the direction of $X$ and uses parallel transport to transport $Y(\gamma(t))$ back to $T_pM$ in order to compare it with $Y(p)$. However, I couldn't connect Definition 5.3.2. with any of those. It would be greatly appreciated if anyone could shed some light on it.

Another question I have is: I didn't come across any examples for computing connections of vector bundles or covariant derivatives on abstract manifolds. Is there a resource where I can find examples or problems to work on? Without seeing these examples, I find it difficult to grasp the complete picture, and I'm starting to forget whatever I've read so far. Thank you in advance.

falamiw
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  • I'm assuming the Riemannian metric here comes from the one induced by the embedding $U\mapsto \mathbb R^n$? If so, working this out in coordinates might be very useful. – Chris Feb 13 '24 at 19:42

2 Answers2

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This is related to the Kozsul formula, also called the fundamental theorem of Riemannian geometry. It gives a formula for computing the unique connection on a Riemannian manifold that preserves the metric and is torsion-free. It is a bit long to write all the details, but see https://en.wikipedia.org/wiki/Fundamental_theorem_of_Riemannian_geometry.

In particular, you get concrete formulas for the Christoffel symbols, and that would allow you to compute many connections and connection forms, in response to your second question.

For the first one, you just need to check that the terms of the Kozsul formula match with the more abstract formula you put there. Let me write out this part.

We want to check that $\nabla_X Y =\gamma^{-1}\theta_{Y, X}$, that is, by definition, for a vector field $Z$, $$ g(\nabla_X Y, Z)=g(\gamma^{-1}\theta_{Y, X}, Z)=\theta_{Y, X}(Z). $$ Now $g(\nabla_X Y, Z)$ is exactly the subject of the Kozsul formula, so we want to check the RHS also produces this result. \begin{align*} \theta_{Y, X}(Z) &= \frac{1}{2} i(X)[{\mathcal L}_Y g + d\theta_Y](Z)\\ &=\frac{1}{2}[({\mathcal L}_Y g)(X, Z) + (d\theta_Y)(X, Z)]\\ &=\frac{1}{2}[({\mathcal L}_Y(g(X, Z))-g(({\mathcal L}_Y X, Z) - g(X, {\mathcal L}_Y Z) + X(\theta_Y(Z)) - Z(\theta_Y (X)) - \theta_Y([X, Z])]\\ &=\frac{1}{2}[Y(g(X, Z)) - g([Y, X], Z) - g(X, [Y, Z]) + X(g(Y, Z)) - Z(g(Y, X)) - g(Y, [X, Z])]\\ &=\frac{1}{2}[X(g(Y, Z)) + Y(g(X, Z)) - Z(g(Y, X)) - g([Y, X], Z) - g( [Y, Z], X)- g([X, Z], Y)], \end{align*} and this exactly the Kozsul formula.

Here we used the definition of the Lie derivative of a tensor, and the formula for exterior differentiation (see https://en.wikipedia.org/wiki/Exterior_derivative).

Three aggies
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  • Much better than working this out in coordinates – Chris Feb 13 '24 at 19:57
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    @Chris I am a big fan though of the beautiful formula $\Gamma_{ij}^k = \frac{1}{2}g^{kl}(g_{il,j}+g_{lj,i}-g_{ij,l})$ for the Christ-awful symbols :) – Three aggies Feb 13 '24 at 20:00
  • I was slogging through the coordinate computation, then you posted your answer and I felt like an idiot. Well done :) – Chris Feb 13 '24 at 20:06
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    I see, thanks. I like it when the community feels so friendly. – Three aggies Feb 13 '24 at 20:10
  • Ended up having to see it through since I was so close to the finish line. It truly was a mess of coordinates lol :0 – Chris Feb 13 '24 at 20:20
  • Thank you very much for your answer, @Threeaggies I face some issue to completely understand your answer. Like I try to google the Lie derivative of a tensor, but find the Wiki. from where it's hard to understand your computation (checking the RHS) of the last paragraph. If you don't mind, can you write which def/rule you use for each lines, please. Thanks for your time and consideration. – falamiw Feb 14 '24 at 15:53
  • I am happy to answer the questions one by one. So for this Lie derivative part, it is the Axiom 3 of the algebraic definition in your Wiki link. It is basically the Leibniz rule. We are subtracting the terms on the right to make them on the other side though. – Three aggies Feb 14 '24 at 16:25
  • Aha, got that. But does $i(X)$ mean contraction here? I guess that's the last piece of confusion. Like isn't $\text{contraction}(T_j^i)=T_i^i$? But the action of the contraction is not clear to me for this case. $i(X){\mathcal L}_Y g + d\theta_Y\stackrel{?}{=}[({\mathcal L}_Y g)(X, Z) + (d\theta_Y)(X, Z)]$. Thanks again. – falamiw Feb 14 '24 at 16:42
  • Contraction is $c$, this one is inner product $i$. They are related but different. $i(X)=i_X$ just inserts $X$ into the first spot for the tensor, as we did do for the above formula. See https://en.wikipedia.org/wiki/Interior_product – Three aggies Feb 14 '24 at 20:12
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So if Three aggies answer was too neat for you here's a coordinate calculation that does pretty much the same thing, with added bonus that you get to keep track of indices. Let $Y=Y^i\partial_i$, $X=X^i\partial_i$, and $g=g_{ij}dx^i\otimes dx^j$. We first have that: \begin{align} \theta_{Y}=g_{ij}Y^idx^j \end{align} so: \begin{align} d\theta_Y=(\partial_{k}g_{ij}Y^i+g_{ij}\partial_kY^i)dx^k\wedge dx^j \end{align} Meanwhile, we have that: \begin{align} \mathscr{L}_Yg=(Y^k\partial_kg_{ij}+(\partial_iY^k)g_{kj}+(\partial_jY^k)g_{ik})dx^i\otimes dx^j \end{align} We see that the contraction with $X$ is given by: \begin{align} i(X)\mathscr{L}_Yg=X^i(Y^k\partial_kg_{ij}+(\partial_iY^k)g_{kj}+(\partial_jY^k)g_{ik})dx^j \end{align} meanwhile: \begin{align} i(X)d\theta_Y=&(\partial_kg_{ij}Y^i+g_{ij}\partial_kY^i)X^kdx^j- (\partial_kg_{ij}Y^i+g_{ij}\partial_kY^i)X^jdx^k\\ =&(\partial_ig_{kj}Y^k+g_{kj}\partial_iY^k-\partial_jg_{ki}Y^k-g_{ki}\partial_jY^k)X^idx^j \end{align} When we add the two together we get: \begin{align} \theta_{X,Y}=X^i\frac{1}{2}\left(Y^k\partial_ig_{kj}+g_{kj}\partial_iY^k-Y^k\partial_jg_{ik}+Y^k\partial_kg_{ij}+g_{kj}\partial_iY^k\right)dx^j \end{align} Then $\gamma^{-1}$ of this is: \begin{align} \gamma^{-1}(\theta_{X,Y})=&\frac{1}{2}X^ig^{jl}\left(g_{kj}\partial_iY^k+g_{kj}\partial_iY^k\right)\partial_l+\frac{1}{2}X^iY^kg^{jl}\left(\partial_ig_{kj}+\partial_kg_{ij}-\partial_jg_{ik}\right)\partial_l\\ =&X^i\left(\partial_iY^l+Y^k\Gamma^l_{ik}\right)\partial_l \end{align} which is precisely the Levi-Civita connection in coordinates.

Edit:

So the map $\gamma:\Omega^1(M)\rightarrow \mathfrak{X}(M)$ is induced by the isomorphism $TM\rightarrow T^*M$. Locally, this is just raising and lowering indices via the metric. So if $Z^i\partial_i$ is a vector field written in coordinates, the one form $\gamma^{-1}(Z^i\partial_i)$ is given in coordinates by $Z^ig_{ij}dx^j$. The inverse is just given by using the inverse metric $g^{ij}$. So if we have a one form $\omega_idx^i$, then the vector field associated to it $\omega_ig^{ij}\partial_j$.

Chris
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  • Thank you very much for your response, @Chris. I guess coordinate calculation make more sense to me. I have some doubts, (i) Does $i(X)$ mean contraction here? (ii)how $i(X)\mathscr{L}Yg$ equal to $X^i(Y^k\partial_kg{ij}+(\partial_iY^k)g_{kj}+(\partial_jY^k)g_{ik})dx^j$, like isn't there should be a wedge? I guess I didn't fully understand how $i(X)$ contract things here. It will be a great help if you explain them a little bit (as I am reading these material by myself, I might be unable to catch simple things, sorry for that). And Thanks again for your time and consideration. – falamiw Feb 14 '24 at 16:21
  • $i(X)$ to me means you just insert $X^i\partial_i$ into $dx^i$. You just applied the rule $dx^k(\partial_j)=\delta^k_j$ – Chris Feb 14 '24 at 17:00
  • @falamiw in particular, there should be no wedge. The Lie derivative of a symmetric two tensor is a symmetric two tensor, so theres no antisymmetric going on. Moreover, even if there was a wedge, the contraction with $X$ would give us a one form, which by definition can't be antisymmetric (or symmetric for that matter) – Chris Feb 15 '24 at 11:33
  • Sorry for late response, I am going through my exam. Okay, I do the whole computation myself but I faced that I didn't understand the isomorphism $\gamma$. Like it eat a vector and spit out $1$-form. But how did you compute the action $\gamma^{-1}(\theta_{X,Y})$ here? inverse metric component make sense but I was unable to undo the action $i(X)g$ in whole or interpret it. And I will give my bounty to your answer as it make sense to me very much. Thanks for your time and help again. – falamiw Feb 17 '24 at 16:55
  • Check the edit, I believe I have answered your question. – Chris Feb 17 '24 at 18:14
  • Can you help me with another question posted here? Or at least give some direction, please – falamiw Feb 19 '24 at 20:26