Computer algebra for calculating curvature when the tensor metric is very big

Question

Is there a computer algebra method to compute the curvature of a Riemannian metric on the plane when the metric tensor has long entries $E,F,G$

The computation by hand is very complicated and long.

I would like to apply this possible software to calculate the Gaussian curvature described in the following posts:

A curvature description for center condition for quadratic vector field

Limit cycles as closed geodesics(2)

Finding a 1-form adapted to a smooth flow

I computed by hand, for two particular vector fields

$V=y\partial_x-x\partial_y$ and $V=y\partial_x -(x+x^2)\partial_y$

Now I need try other vector fields so I need computer help.

Thank you for your help, comments or answers.

Answer 1

Try SageManifolds http://sagemanifolds.obspm.fr/

See this example (there are several others) for how to compute the curvature tensor from the metric

http://nbviewer.jupyter.org/github/sagemanifolds/SageManifolds/blob/master/Worksheets/v1.0/SM_Schwarzschild.ipynb

Hint: it's just

R = g.riemann()
R.display()

EDIT: Here's a complete example

You can open the CoCalc worksheet here:

https://cocalc.com/app#projects/38691bca-4290-4f77-a3b0-bee86df3d85f/files/sagemanifold.sagews

M = Manifold(2, 'M', r'\mathbb{R}^2')
coords.<x,y> = M.chart()
g = M.riemannian_metric('g', latex_name=r'g')

E(x, y) = e^(x + y)
F(x, y) = e^(x^2 + y^2)
G(x, y) = e^(x + y)

g[0, 0] = E
g[0, 1] = F
g[1, 1] = G

R = g.riemann()

R.display()

Answer 2

There is some Maple code at

https://github.com/NeilStrickland/genus2/blob/master/maple/embedded/curvature.mpl

It is a fairly straightforward translation of the definitions. I am not clear whether that is what you need. If not, perhaps you could be more specific.