## Dimension 2

The equation for the perimeter of an ellipse is

where *a* is the semimajor axis, *e* is eccentricity, and *E* is a special function. The equation is simple, in the sense that it has few terms, but it is not elementary, because it depends on an advanced function, the complete elliptic integral of the second kind.

However, there is an approximation for the perimeter that is both simple and elementary:

## Dimension 3

The generalization of an ellipse to three dimensions is an ellipsoid. The surface area of an ellipsoid is neither simple nor elementary. The surface area *S* is given by

where *E* is *incomplete* elliptic integral of the second kind and *F* is the incomplete elliptic integral of the first kind.

However, once again there is an approximation that is simple and elementary. The surface area approximately

where *p* = 1.6075.

Notice the similarities between the approximation for the perimeter of an ellipse and the approximation for the area of an ellipsoid. The former is the perimeter of a unit circle times a kind of mean of the axes. The latter is the area of a unit sphere times a kind of mean of the products of pairs of axes. The former uses a *p*-mean with *p* = 1.5 and the latter uses a *p*-mean with *p* = 1.6075. More on such means here.

## Dimensions 4 and higher

The complexity of expressions for the surface area of an ellipsoid apparently increase with dimension. The expression get worse for hyperellipsoids, i.e. *n*-ellipsoids for *n* > 3. You can find such expressions in [1]. More of that in just a minute.

### Conjecture

It is natural to conjecture, based on the approximations above, that the surface area of an *n*-ellipsoid is the area of a unit sphere in dimension *n* times the *p*-mean of all products of *n*-1 semiaxes for some value of *p*.

For example, the surface area of an ellipsoid in 4 dimensions might be approximately

for some value of *p*.

Why this form? Permutations of the axes do not change the surface area, so we’d expect permutations not to effect the approximation either. (More here.) Also, we’d expect from dimensional analysis for the formula to involve products of *n*-1 terms since the result gives *n*-1 dimensional volume.

Surely I’m not the first to suggest this. However, I don’t know what work has been done along these lines.

### Exact results

In [1] the author gives some very complicated but general expressions for the surface area of a hyperellipsoid. The simplest of his expression involves probability:

where the *X*s are independent normal random variables with mean 0 and variance 1/2.

At first it may look like this can be simplified. The sum of normal random variables is a normal random variable. But the squares of normal random variables are not normal, they’re gamma random variables. The sum of gamma random variables is a gamma random variable, but that’s only if the variables have the same scale parameter, and these do not unless all the semiaxes, the *q*s, are the same.

You could use the formula above as a way to approximate *S* via Monte Carlo simulation. You could also use asymptotic results from probability to get approximate formulas valid for large *n*.

[1] Igor Rivin. Surface area and other measures of ellipsoids. Advances in Applied Mathematics 39 (2007) 409–427

I really want to factor the product of the semi-major axes out of that average, which would leave a p-average of the reciprocals of the semi-major axes as the “correction factor”.

I don’t see how it immediately makes it any more intuitive, but it feels like the first step on the way to discussing how curvature affects surface area.