## Tensor.

The true but unenlightening answer first: a tensor is a regular, rectangular grid of numbers. The most common kind is a two-dimensional grid, so that it looks like a matrix, or like the times tables. It might be square, with as many rows as columns, or it might be rectangular.

It can also be one-dimensional, looking like a row or a column of numbers. Or it could be three-dimensional, rows and columns and whole levels of numbers. We don’t try to visualize that. It can be what we call zero-dimensional, in which case it just looks like a solitary number. It might be four- or more-dimensional, although I confess I’ve never heard of anyone who actually writes out such a thing. It’s just so hard to visualize.

You can add and subtract tensors if they’re of compatible sizes. You can also do something like multiplication. And this does mean that tensors of compatible sizes will form a ring. Of course, that doesn’t say why they’re *interesting*.

Tensors are useful because they can describe spatial relationships efficiently. The word comes from the same Latin root as “tension”, a hint about how we can imagine it. A common use of tensors is in describing the stress in an object. Applying stress in different directions to an object often produces different effects. The classic example there is a newspaper. Rip it in one direction and you get a smooth, clean tear. Rip it perpendicularly and you get a raggedy mess. The stress tensor represents this: it gives some idea of how a force put on the paper will create a tear.

Tensors show up a lot in physics, and so in mathematical physics. Technically they show up everywhere, since vectors and even plain old numbers (scalars, in the lingo) are kinds of tensors, but that’s not what I mean. Tensors can describe efficiently things whose magnitude and direction changes based on where something is and where it’s looking. So they are a great tool to use if one wants to represent stress, or how well magnetic fields pass through objects, or how electrical fields are distorted by the objects they move in. And they describe space, as well: general relativity is built on tensors. The mathematics of a tensor allow one to describe how space is shaped, based on how to measure the distance between two points in space.

My own mathematical education happened to be pretty tensor-light. I never happened to have courses that forced me to get good with them, and I confess to feeling intimidated when a mathematical argument gets deep into tensor mathematics. Joseph C Kolecki, with NASA’s Glenn (Lewis) Research Center, published in 2002 a nice little booklet “An Introduction to Tensors for Students of Physics and Engineering”. This I think nicely bridges some of the gap between mathematical structures like vectors and matrices, that mathematics and physics majors know well, and the kinds of tensors that get called tensors and that can be intimidating.

I think the most important thing to learn is and ‘conceptual leap’ to think of a tensor not of a ‘matrix’ but of that abstract object of which the ‘matrix’ is just one possible representation, just as ‘visualizing’ a vector as an ‘arrow’ in some hyperspace instead of a bunch of numbers.

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I think you’re right that understanding a matrix is just one way to represent a tensor is the great conceptual leap. The trouble is having a good explanation for what the tensor is, separate from its representations. Although come to think of it I can’t remember when I managed the conceptual leap between a vector as a set of numbers to a vector being some direction and length in space, either.

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