So far we’ve been exploring exterior calculus purely in the smooth setting. Unfortunately this theory was developed by some old-timers who did not know anything about computers, hence it cannot be used directly by machines that store only a finite amount of information. For instance, if we have a smooth vector field or a smooth 1-form we can’t possibly store the direction of every little “arrow” at each point — there are far too many of them! Instead, we need to keep track of a discrete (or really, *finite*) number of pieces of information that capture the essential behavior of the objects we’re working with; we call this scheme *discrete exterior calculus* (or *DEC* for short). The big secret about DEC is that it’s literally *nothing more* than the good-old fashioned (continuous) exterior calculus we’ve been learning about, except that we integrate differential forms over elements of our mesh.

__Discrete Differential Forms__

One way to encode a 1-form might be to store a finite collection of “arrows” associated with some subset of points. Instead, we’re going to do something a bit different: we’re going to *integrate* our 1-form over each edge of a mesh, and store the resulting *numbers* (remember that the integral of an \(n\)-form always spits out a single number) on the corresponding edges. In other words, if \(\alpha\) is a 1-form and \(e\) is an edge, then we’ll associate the number

\[ \hat{\alpha}_e := \int_e \alpha \]

with \(e\), where the use of the hat (\(\ \hat{}\ \)) is supposed to suggest a *discrete* quantity (not to be confused with a unit-length vector).

Does this procedure seem a bit abstract to you? It shouldn’t! Think about what this integral represents: it tells us how strongly the 1-form \(\alpha\) “flows along” the edge \(e\) *on average*. More specifically, remember how integration of a 1-form works: at each point along the edge we take the vector *tangent* to the edge, stick it into the 1-form \(\alpha\), and sum up the resulting values — each value tells us something about how well \(\alpha\) “lines up” with the direction of the edge. For instance, we could approximate the integral via the sum

\[ \int_e \alpha \approx |e|\left(\frac{1}{N} \sum_{i=1}^N \alpha_{p_i}(\hat{e})\right), \]

where \(|e|\) denotes the length of the edge, \(\{p_i\}\) is a sequence of points along the edge, and \(\hat{e} := e/|e|\) is a unit vector tangent to the edge:

Of course, this quantity tells us *absolutely nothing* about the strength of the “flow” *orthogonal* to the edge: it could be zero, it could be enormous! We don’t really know, because we didn’t take any measurements along the orthogonal direction. However, the hope is that some of this information will still be captured by nearby edges (which are most likely not parallel to \(e\)).

More generally, a \(k\)-form that has been integrated over each \(k\)-dimensional cell (edges in 1D, faces in 2D, etc.) is called a *discrete differential \(k\)-form*. (If you ever find the distinction confusing, you might find it helpful to substitute the word “integrated” for the word “discrete.”) In practice, however, not every discrete differential form has to originate from a continuous one — for instance, a bunch of arbitrary values assigned to each edge of a mesh is a perfectly good discrete 1-form.

__Orientation__

One thing you may have noticed in all of our illustrations so far is that each edge is marked with a little arrow. Why? Well, one thing to remember is that *direction* matters when you integrate. For instance, the fundamental theorem of calculus (and common sense) tells us that the total change as you go from \(a\) to \(b\) is the opposite of the total change as you go from \(b\) to \(a\):

\[ \int_a^b \frac{\partial\phi}{\partial x} dx = \phi(b)-\phi(a) = -(\phi(a)-\phi(b)) = -\int_b^a \frac{\partial\phi}{\partial x} dx. \]

Said in a much less fancy way: the elevation gain as you go from Pasadena to Altadena is 151 meters, so of the elevation “gain” in the other direction must be -151 meters! Just keeping track of the number 151 does you little good — you have to say what that quantity represents.

Therefore, when we store a discrete differential form it’s not enough to just store a number: we also have to specify a canonical *orientation* for each element of our mesh, corresponding to the orientation we used during integration. For an edge we’ve already seen that we can think about orientation as a little arrow pointing from one vertex to another — we could also just think of an edge as an *ordered* pair \((i,j)\), meaning that we always integrate from \(i\) to \(j\).

More generally, suppose that each element of our mesh is an *oriented \(k\)-simplex* \(\sigma\), i.e., a collection of \(k+1\) vertices \(p_i \in \mathbb{R}^n\) given in some fixed order \((p_1, \ldots, p_{k+1})\). The geometry associated with \(\sigma\) is the *convex combination* of these points:

\[ \left\{ \sum_{i=1}^{k+1} \lambda_i p_i \left| \sum_{i=1}^{k+1} \lambda_i = 1 \right. \right\} \subset \mathbb{R}^n \]

(Convince yourself that a 0-simplex is a vertex, a 1-simplex is an edge, a 2-simplex is a triangle, and a 3-simplex is a tetrahedron.)

Two oriented \(k\)-simplices have the same orientation if and only if the vertices of one are an *even* permutation of the vertices of another. For instance, the triangles \((p_1, p_2, p_3)\) and \((p_2, p_3, p_1)\) have the same orientation; \((p_1, p_2, p_3)\) and \((p_2, p_1, p_3)\) have opposite orientation.

If a simplex \(\sigma_1\) is a (not necessarily proper) subset of another simplex \(\sigma_2\), then we say that \(\sigma_1\) is a *face* of \(\sigma_2\). For instance, every vertex, edge, and triangle of a tetrahedron \(\sigma\) is a face of \(\sigma\); as is \(\sigma\) itself! Moreover, the orientation of a simplex *agrees with* the orientation of one of its faces as long as we see an even permutation on the shared vertices. For instance, the orientations of the edge \((p_2,p_1)\) and the triangle \((p_1,p_3,p_2)\) agree. Geometrically all we’re saying is that the two “point” in the same direction (as depicted above). To keep yourself sane while working with meshes, the most important thing is to *pick and orientation and stick with it*!

So in general, how do we integrate a \(k\)-form over an oriented \(k\)-simplex? Remember that a \(k\)-form is going to “eat” \(k\) vectors at each point and spit out a number — a good canonical choice is to take the ordered collection of edge vectors \((p_2 – p_1, \ldots, p_{k+1}-p_1)\) and orthogonalize them (using, say the Gram-Schmidt algorithm) to get vectors \((u_1, \ldots, u_n)\). This way the sign of the integrand changes whenever the orientation changes. Numerically, we can then approximate the integral via a sum

\[ \int_\sigma \alpha \approx \frac{|\sigma|}{N} \sum_{i=1}^N \alpha_{p_i}(u_1, \ldots, u_n) \]

where \(\{p_i\}\) is a (usually carefully-chosen) collection of sample points. (Can you see why the orientation of \(\sigma\) affects the sign of the integrand?) Sounds like a lot of work, but in practice one rarely constructs discrete differential forms via integration: more often, discrete forms are constructed via input data that is already discrete (e.g., vertex positions in a triangle mesh).

By the way, what’s a discrete 0-form? Give up? Well, it must be a 0-form (i.e., a function) that’s been integrated over every 0-simplex (i.e., vertex) of a mesh:

\[ \hat{\phi}_i = \int_{v_i} \phi \]

By convention, the integral of a function over a zero-dimensional set is simply the value of the function at that point: \(\hat{\phi}_i = \phi(v_i)\). In other words, in the case of 0-forms there is no difference between storing point samples and storing integrated quantities: the two coincide.

It’s also important to remember that differential forms don’t have to be *real*-valued. For instance, we can think of a map \(f: M \rightarrow \mathbb{R}^3\) that encodes the geometry of a surface as an \(\mathbb{R}^3\)-valued 0-form; its differential \(df\) is then an \(\mathbb{R}^3\)-valued 1-form, etc. Likewise, when we say that a discrete differential form is a *number* stored on every mesh element, the word “number” is used in a fairly loose sense: a number could be a real value, a vector, a complex number, a quaternion, etc. For instance, the collection of \((x,y,z)\) vertex coordinates of a mesh can be viewed as an \(\mathbb{R}^3\)-valued discrete 0-form (namely, one that discretizes the map \(f\)). The only requirement, of course, is that we store the same *type* of number on each mesh element.

__The Discrete Exterior Derivative__

One of the main advantages of working with integrated (i.e., “discrete”) differential forms instead of point samples is that we can easily take advantage of Stokes’ theorem. Remember that Stokes’ theorem says

\[ \int_\Omega d\alpha = \int_{\partial\Omega} \alpha, \]

for any \(k\)-form \(\alpha\) and \(k+1\)-dimensional domain \(\Omega\). In other words, we can integrate the derivative of a differential form as long as we know its integral along the boundary. But that’s *exactly* the kind of information encoded by a discrete differential form! For instance, if \(\hat{\alpha}\) is a discrete 1-form stored on the three edges of a triangle \(\sigma\), then we have

\[ \int_\sigma d\alpha = \int_{\partial\sigma} \alpha = \sum_{i=1}^3 \int_{e_i} \alpha = \sum_{i=1}^3 \hat{\alpha}_i. \]

In other words, we can *exactly* evaluate the integral on the left by just adding up three numbers. Pretty cool! In fact, the thing on the left is *also* a discrete differential form: it’s the 2-form \(d\alpha\) integrated over the only triangle in our mesh. So for convenience, we’ll call this guy “\(\hat{d}\hat{\alpha}\)”, and we’ll call the operation \(\hat{d}\) the *discrete exterior derivative*. (In the future we will drop the hats from our notation when the meaning is clear from context.) In other words, the discrete exterior derivative takes a \(k\)-form that has *already been integrated* over each \(k\)-simplex and applies Stokes’ theorem to get the integral of the derivative over each \(k+1\)-simplex.

In practice (i.e., in code) you can see how this operation might be implemented by simply taking local sums over the appropriate mesh elements. However, in the example above we made life particularly easy on ourselves by giving each edge an orientation that agrees with the orientation of the triangle. Unfortunately assigning a consistent orientation to every simplex is not always possible, and in general we need to be more careful about *sign* when adding up our piecewise integrals. For instance, in the example below we’d have

\[ (\hat{d}\hat{\alpha})_1 = \hat{\alpha}_1 + \hat{\alpha}_2 + \hat{\alpha}_3 \]

and

\[ (\hat{d}\hat{\alpha})_2 = \hat{\alpha}_4 + \hat{\alpha}_5 - \hat{\alpha}_2. \]

__Discrete Hodge Star__

As hinted at above, a discrete \(k\)-form captures the behavior of a continuous \(k\)-form along \(k\) directions, but not along the remaining \(n-k\) directions — for instance, a discrete 1-form in 2D captures the flow along edges but not in the orthogonal direction. If you paid attention to our discussion of Hodge duality, this story starts to sound familiar! To capture Hodge duality in the discrete setting, we’ll need to define a *dual mesh*. In general, the dual of an \(n\)-dimensional simplicial mesh identifies every \(k\)-simplex in the primal (i.e., original) mesh with a unique \((n-k)\)-cell in the dual mesh. In a two-dimensional *simplicial* mesh, for instance, primal vertices are identified with dual faces, primal edges are identified with dual edges, and primal faces are identified with dual vertices. Note, however, that the dual cells are not always simplices! (See above.)

So how do we talk about Hodge duality in discrete exterior calculus? Quite simply, the *discrete Hodge dual* of a (discrete) \(k\)-form on the primal mesh is an \((n-k)\)-form on the dual mesh. Similarly, the Hodge dual of an \(k\)-form on the dual mesh is a \(k\)-form on the primal mesh. Discrete forms on the primal mesh are called *primal forms* and discrete forms on the dual mesh are called *dual forms*. Given a discrete form \(\hat{\alpha}\) (whether primal or dual), its Hodge dual is typically written as \(\hat{\star} \hat{\alpha}\).

Unlike continuous forms, discrete primal and dual forms live in different places (so for instance, discrete primal \(k\)-forms and dual \(k\)-forms cannot be added to each other). In fact, primal and dual forms often have different physical interpretations. For instance, a primal 1-form might represent the total circulation along edges of the primal mesh, whereas in the same context a dual 1-form might represent the total flux through the corresponding dual edges (see illustration above).

Of course, these two quantities (flux and circulation) are closely related, and naturally leads into one definition for a discrete Hodge star called the *diagonal Hodge star*. Consider a primal \(k\)-form \(\alpha\). If \(\hat{\alpha}_i\) is the value of \(\hat{\alpha}\) on the \(k\)-simplex \(\sigma_i\), then the diagonal Hodge star is defined by

\[ \hat{\star} \hat{\alpha}_i = \frac{|\sigma_i^\star|}{|\sigma_i|} \hat{\alpha}_i \]

for all \(i\), where \(|\sigma|\) indicates the (unsigned) volume of \(\sigma\) (which by convention equals *one* for a vertex!) and \(|\sigma^\star|\) is the volume of the corresponding dual cell. In other words, to compute the dual form we simply multiply the scalar value stored on each cell by the ratio of corresponding dual and primal volumes.

If we remember that a discrete form can be thought of as a continuous form integrated over each cell, this definition for the Hodge star makes perfect sense: the primal and dual quantities should have the same *density*, but we need to account for the fact that they are integrated over cells of different volume. We therefore normalize by a *ratio* of volumes when mapping between primal and dual. This particular Hodge star is called *diagonal* since the \(i\)th element of the dual differential form depends only on the \(i\)th element of the primal differential form. It’s not hard to see, then, that Hodge star taking dual forms to primal forms (the *dual Hodge star*) is the inverse of the one that takes primal to dual (the *primal Hodge star*).

__That’s All, Folks!__

Hey, wait a minute, what about our other operations, like the wedge product (\(\wedge\))? These operations can certainly be defined in the discrete setting, but we won’t go into detail here — the basic recipe is to integrate, integrate, integrate. Actually, even in *continuous* exterior calculus we omitted a couple operations like the *Lie derivative* (\(\mathcal{L}_X\)) and the *interior product* (\(i_\alpha\)). Coming up with a complete discrete calculus where the whole cast of characters \(d\), \(\wedge\), \(\star\), \(\mathcal{L}_X\), \(i_\alpha\), etc., plays well together is an active and ongoing area of research, which may be of interest to aspiring young researchers like you (yes, *you*)!