# Divergence / Gradient / Laplace Operator

Yao Yao on June 11, 2018

## Divergence

Quote from Wikipedia: Divergence:

Let $x$, $y$, $z$ be a system of Cartesian coordinates in 3-dimensional Euclidean space, and let $\mathbf{i}$, $\mathbf{j}$, $\mathbf{k}$ be the corresponding basis of unit vectors. The divergence of a continuously differentiable vector field $F = U \mathbf{i} + V \mathbf{j} + W \mathbf{k}$ is defined as the scalar-valued function:

1. 写法。$F = U \mathbf{i} + V \mathbf{j} + W \mathbf{k}$ 其实就是 $\vec F = \icol{U \newline V \newline W}$，它其实是一个 vector
2. 这里 $\nabla \cdot \mathbf {F}$ 明显不是 dot product，但是计算方法类似，最后的结果是一个 scalar
3. Gradient 的写法 $\nabla f$ 不带这个 dot

## Divergence 的物理意义

Quote from Erik Anson’s answer on Quora:

Imagine a fluid, with the vector field representing the velocity of the fluid at each point in space. Divergence measures the net flow of fluid out of (i.e., diverging from) a given point. If fluid is instead flowing into that point, the divergence will be negative.

A point or region with positive divergence is often referred to as a “source” (of fluid, or whatever the field is describing), while a point or region with negative divergence is a “sink”.

The bigger the flux density (positive or negative), the stronger the flux source or sink. A div of zero means there’s no net flux change in side the region.

## Laplace Operator

which happens to be the $lap$ of $f(x, y, z)$.

I.e. $\operatorname{lap} f = \operatorname{div}(\operatorname{grad} f)$

• 如果你在 $f$ 的 local minimum $a$，你周围的 gradient 全部流出 $a$ (任意方向都是 ascending)，divergence 是 highly positive，所以 $a$ 是 gradient 的 source
• 如果你在 $f$ 的 local maximum $b$，你周围的 gradient 全部流入 $b$ (任意方向都是 descending)，divergence 是 highly negative，所以 $b$ 是 gradient 的 sink

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