Compute Gradient in GR: Step-by-Step Guide

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In summary, the conversation discusses the computation of the extrinsic curvature using a given metric describing the interior and exterior of a bubble. The formula for the extrinsic curvature is provided, along with the norm of the bubble and the nonzero Christoffel symbols of the metric. The Einstein summation convention is used to sum over all possible values of repeated indices to calculate the 16 components of the extrinsic curvature.
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John Greger
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I'm trying to compute the extrinsic curvature. I have the formula and everything I need to plug into the formula. But I get confused when executing this calculation..
I'm trying to compute the extrinsic curvature. I have the formula and everything I need to plug into the formula. But I get confused when executing this calculation..

I have that ##ds^2_{interior} = -u(r)dt^2 + (u(r))^{-1} dr^2 + r^2 d\Omega_3^2##. This is a metric describing the interior and exterior of a bubble. The extrinsic curvacture is given by
\begin{equation}
K_{ab} = N_{\mu; \nu} (\frac{\partial x^\mu}{\partial y^a})(\frac{\partial x^\nu}{\partial y^b}) = ( \frac{\partial N_\mu}{\partial x^\nu } - \Gamma^{k}_{\mu \nu} N_{k}) )(\frac{\partial x^\mu}{\partial y^a})(\frac{\partial x^\nu}{\partial y^b}).
\end{equation}

##N_{\mu; \nu}## is the norm of the bubble which is given by ##N_a = (-\dot{R}, \dot{T},0,0,0)##. Since we are looking for dyamical on the brane, we don't care about the angular part of the metric: ##(t,r, \Omega) \rightarrow (T(\tau), R(\tau), \Omega)##.

##x^\mu## labels bulk metric (I think##(\tau, r, \Omega)##) and ##y^a## labels coordinate on the brane (I think ## (T(\tau), R(\tau), \Omega)##.

The nonzero christoffel sumbols of the Metric are ##\Gamma^r_{rr} = \frac{\partial u / \partial r}{u(r)} = \Gamma^t_{rt}##.

I don't know howto substitute all of this into (1). Should I sum all possible combinations of indices or should I sum the following two combinations ##(\mu, \nu) = (\tau, r) ; (a,b)= (T(\tau), R(\tau))## and ##(\mu, \nu) = (r, \tau) ; (a,b)= (R(\tau), T(\tau))##?

If I get some initial help here it will be straight forward to take it from there I think.

P.S the answer should be $$K_{a,b} = -\frac{1}{u \dot{T}}[\ddot{R} + (1/2) \frac{\partial u}{\partial R}] + u(R) \dot{T} R$$ but I cannot really arrive at this. Any help to get this expression is much apprichiated.
 
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  • #2
##N_{\mu;\nu}## is the covariant derivative of ##N_\mu##.
 
  • #3
martinbn said:
##N_{\mu;\nu}## is the covariant derivative of ##N_\mu##.
Hi! Many thanks for your answer. Yes indeed, might have abused language. I believe I expanded it in equation 1 the right way. But I feel that I am confused about how to sum the indices accordingly..
 
  • #4
You sum the indices with the Einstein summation convention, which basically says you sum over all possible values of repeated indices.

a and b appear on the left hand side, so you are computing 16 quantities, as a and b both vary from 0 to 3 (or possibly 1 to 4, depending on your notation).

For each specific value of a and b, you have one component of ##K_{ab}##. To get the value of that component, you sum over all values of k, ##\mu##, and ##\nu##. Thus if you wrote it out longhand, in general each of the 16 components of ##K_{ab}## would be the sum of 64 terms. Hopefully, though, your metric is simple enough that many of the terms are zero.
 
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1. What is gradient in general relativity?

The gradient in general relativity is a mathematical concept that describes the change in a physical quantity (such as energy or mass) with respect to a specific coordinate system. In general relativity, the gradient is used to calculate the change in spacetime curvature, which is responsible for the effects of gravity.

2. Why is it important to compute gradient in general relativity?

Computing the gradient in general relativity allows us to understand how spacetime curvature changes in different regions, which is crucial for understanding the behavior of matter and energy in the presence of gravity. It also helps us make predictions and calculations about the motion of objects in the universe.

3. What are the steps for computing gradient in general relativity?

The steps for computing gradient in general relativity include: 1) defining the coordinate system, 2) calculating the metric tensor, 3) calculating the Christoffel symbols, 4) calculating the Riemann curvature tensor, and 5) using the Riemann curvature tensor to calculate the gradient. These steps can be complex and may require advanced mathematical knowledge.

4. Are there any tools or software available for computing gradient in general relativity?

Yes, there are several software packages and tools available for computing gradient in general relativity, such as Mathematica, Maple, and Python libraries like SymPy. These tools can help simplify the calculations and make the process more efficient.

5. What are some real-world applications of computing gradient in general relativity?

Some real-world applications of computing gradient in general relativity include predicting the motion of celestial bodies, studying the behavior of black holes, and understanding the expansion of the universe. It also has applications in engineering, such as in the design of space missions and satellites.

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