Soft Question: Difficult to Visualize Areas of Mathematics Yesterday I came across this webpage, which describes a recent (successful) attempt to visualize isometric embeddings of flat tori in 3D Euclidean space. The webpage and associated paper discuss the difficulty in creating this visualization, which was why it hadn't been done before. Are there other problems or objects in mathematics that are similarly hard to visualize?
 A: Yes, there are plenty. This is true even if we restrict ourselves to geometrical objects. On one hand, visualizing geometrical objects in $4$ or more dimensions can be (an usually is) very challenging, even for simple ones like the tesseract (alias $4$-hypercube):

On the other hand, we can only really depict geometry over the real numbers. For example, how do you visualize the locus $x + 2 y = 0$ with $x,y \in \Bbb{C}$? (hint: a complex plane!) What about the locus $x^2 + y^2 = r$?
Even worse, getting a decent idea of what a scheme (a kind of geometrical object) looks like can be quite hard. A classical example (an one of my favourites) is the picture of $\text{Spec}(\Bbb{Z}[X])$ from Mumford's red book

of which you can find a nice explanation in this old blog post.

Note that there is quite a difference between these examples and the one you cited, though, in that these are (in a sense) even "harder" to visualize.
What I mean is that these objects cannot be faithfully represented in the "usual" $2$ or $3$ dimensional geometry, and all we can do is associate some images to them from which we can glean some (hopefully a lot) useful information).
On the other hand, what I understand from the webpage you cited is that the method used by Nash and Kuiper to prove the existence of isometric embeddings of flat tori in $3$ dimensional space was ineffective. This is a bit like being able to prove that a certain quantity is bounded, but being unable to tell exactly what the bound is. Sure, you cannot provide an exact picture following the method described in that page because you would have to depict a limit shape, but this is a finer issue: you can still get a really good idea of what that shape is and, given enough computational power, you can approximate it arbitrarily better.
