$\gdef\div{\text{div}} \gdef\vol{\text{Vol}}$ We first finish up the derivation of Laplacian last time. See the lecture note 2020-02-07, Friday. Then, we introduce two vector operations, divergence and curl. Finally, we give a summary of the mathematical treatment of tensor analysis.

Then, we will follow Boas 10.9 and 10.10, to introduce the physic-engineer notations: the nabla operator $\gdef\b{\mathbf} \b \nabla$.

In Cartesian coordinate, the differential of a function $f$ is $$ df = \sum_i \frac{\df }{\d x_i} dx_i. $$

In general coordinate $(u_1, \cdots, u_n)$, the differential of a function $f$ is $df = \sum_i \frac{\d f }{\d u_i} d u_i.$

You can specify the differential of a function directly: $df$ at a point $p \in \R^n$ is a linear function on $T_p \R^n$, $df(p) \in (T_p \R^n)^*$. It does the following $df(p) : \b v_p \mapsto \b v_p(f), \quad \b v_p \in T_p \R^n$ where $\b v_p(f)$ is the directional derivative of $f$ along $\b v_p$.

In Cartesian coordinate, the gradient of a function is $\gdef\grad{\text{ grad } } \grad f = \sum_i \frac{\d f}{\d x_i} \frac{\d }{\d x_i}.$

In general coordinate, the gradient of a function is more complicated $\grad f = \sum_{i,j} g^{ij} \frac{\d f}{\d u_i} \frac{\d }{\d u_j},$ where $g^{ij}$ is the entry of the inverse matrix of the matrix $[g_{kl}]$. And it just happens that, for Cartesian coordinate, $g^{ij} = \delta_{ij}$.

Note that $g_{ij}$ and $g^{ij}$ depends on the coordinate system. $g_{ij} = g(\frac{\d}{\d u_i}, \frac{\d}{\d u_j}), \quad g^{ij} = g^*(d u_i, d u_j).$

Notation $\nabla f = \grad f.$

Let $\R^n$ be the flat space, with standard coordinates $(x_1, \cdots, x_n)$.

Let $\b V$ be a vector field on $\R^n$, that is, for each point $p \in \R^n$, we specify a tangent vector $\b V(p) = \sum_{i=1}^n V^i(p) \d_i \in T_p \R^n.$ We require that $V^i(p)$ varies smoothly with respect to $p$.

The divergence of $\b V$ is a function on $\R^n$, $\div(\b V) = \sum_{i=1}^n \d_i( V^i )$ recall that $V^i$ is a function on $\R^n$, and $\d_i$ is taking the partial derivative with respect to $x_i$.

Notation $\nabla \cdot \b V = \div (\b V).$

What does divergence mean? Geometrically, it measure the relative change-rate of the volume of an infinitesimal cube situated at point $p$. Suppose $\Phi^t: \R^n \to \R^n$ is the flow generated by $\b V$ (every point moves as dictated by $\b V$). And let $C= C(p, \epsilon)$ be a cube of side-length $\epsilon$, center at $p$. Then, we have the geometrical interpretation as $\div(\b V) = \lim_{\epsilon \to 0} \frac{1}{\vol(C)} \frac{d \vol(\Phi^t(C))}{dt} \vert_{t=0}.$ That is why, if $S \subset \R^n$ is an open domain, we can compute the change-rate of the volume of $S$ by $\frac{d \vol(\Phi^t(S)) } {dt}\vert_{t=0} = \int_{S} \div(\b V)(\b x) \, d \vol(\b x).$

In curvilinear coordinate. The formula for computing the divergence is the following, suppose $\b V = \sum_i V^i \frac{\d }{\d u_i}$, then $\div (\b V) = \sum_{i=1}^n \frac{1}{\sqrt{|g|}} \frac{\d (\sqrt{|g|} V^i)}{\d u_i}$

Exercise: prove that for any compactly supported function $\varphi$ ¹⁾, we have $\int_{\R^n} (\nabla \cdot \b V)(u) f(u) \sqrt{|g|(u)} du_1\cdots d u_n = \int_{\R^n} g(\b V, \nabla f(u)) \sqrt{|g|(u)} du_1\cdots d u_n$