# Vector calculus

Coordinate systems
Vectors Coulomb's Law
Material from Vectors was moved here.
Here we extend the concept of vector to that of the vector field. A familiar example of a vector field is wind velocity: It has direction and magnitude, which makes it a vector. But it also depends on position (and ultimately on time). Wind velocity is a function of (x,y,z) at any given time, equivalently we can say that wind velocity is a time-dependent field: ${\vec {V}}_{wind}={\vec {V}}({\vec {r}},t)$.

### Derivative of a vector valued function

Let $\mathbf {a} (x)\,$ be a vector function that can be represented as
    $\mathbf {a} (x)=a_{1}(x)\mathbf {e} _{1}+a_{2}(x)\mathbf {e} _{2}+a_{3}(x)\mathbf {e} _{3}\,$
where $x\,$ is a scalar.
Then the derivative of $\mathbf {a} (x)\,$ with respect to $x\,$ is
    ${\cfrac {d\mathbf {a} (x)}{dx}}=\lim _{\Delta x\rightarrow 0}{\cfrac {\mathbf {a} (x+\Delta x)-\mathbf {a} (x)}{\Delta x}}={\cfrac {da_{1}(x)}{dx}}\mathbf {e} _{1}+{\cfrac {da_{2}(x)}{dx}}\mathbf {e} _{2}+{\cfrac {da_{3}(x)}{dx}}\mathbf {e} _{3}~.$
Note: In the above equation, the unit vectors $\mathbf {e} _{i}$ (i=1,2,3) are assumed constant. If $\mathbf {a} (x)\,$ and $\mathbf {b} (x)\,$ are two vector functions, then from the chain rule we get
    ${\begin{aligned}{\cfrac {d({\mathbf {a} }\cdot {\mathbf {b} })}{x}}&={\mathbf {a} }\cdot {\cfrac {d\mathbf {b} }{dx}}+{\cfrac {d\mathbf {a} }{dx}}\cdot {\mathbf {b} }\\{\cfrac {d({\mathbf {a} }\times {\mathbf {b} })}{dx}}&={\mathbf {a} }\times {\cfrac {d\mathbf {b} }{dx}}+{\cfrac {d\mathbf {a} }{dx}}\times {\mathbf {b} }\\{\cfrac {d[{\mathbf {a} }\cdot {({\mathbf {a} }\times {\mathbf {b} })}]}{dt}}&={\cfrac {d\mathbf {a} }{dt}}\cdot {({\mathbf {b} }\times {\mathbf {c} })}+{\mathbf {a} }\cdot {\left({\cfrac {d\mathbf {b} }{dt}}\times {\mathbf {c} }\right)}+{\mathbf {a} }\cdot {\left({\mathbf {b} }\times {\cfrac {d\mathbf {c} }{dt}}\right)}\end{aligned}}$

### Scalar and vector fields

Let $\mathbf {x} \,$ be the position vector of any point in space. Suppose that there is a scalar function ($g\,$) that assigns a value to each point in space. Then
    $g=g(\mathbf {x} )\,$
represents a scalar field. An example of a scalar field is the temperature. See Figure4(a).
If there is a vector function ($\mathbf {a} \,$) that assigns a vector to each point in space, then
    $\mathbf {a} =\mathbf {a} (\mathbf {x} )\,$
represents a vector field. An example is the displacement field. See Figure 4(b).

### Gradient of a scalar field

Let $\varphi (\mathbf {x} )\,$ be a scalar function. Assume that the partial derivatives of the function are continuous in some region of space. If the point $\mathbf {x} \,$ has coordinates ($x_{1},x_{2},x_{3}\,$) with respect to the basis ($\mathbf {e} _{1},\mathbf {e} _{2},\mathbf {e} _{3}\,$), the gradient of $\varphi \,$ is defined as
    ${\boldsymbol {\nabla }}{\varphi }={\frac {\partial \varphi }{\partial x_{1}}}~\mathbf {e} _{1}+{\frac {\partial \varphi }{\partial x_{2}}}~\mathbf {e} _{2}+{\frac {\partial \varphi }{\partial x_{3}}}~\mathbf {e} _{3}~.$
In index notation,
    ${\boldsymbol {\nabla }}{\varphi }\equiv \varphi _{,i}~\mathbf {e} _{i}~.$
The gradient is obviously a vector and has a direction. We can think of the gradient at a point being the vector perpendicular to the level contour at that point.
It is often useful to think of the symbol ${\boldsymbol {\nabla }}$ as an operator of the form
    ${\boldsymbol {\nabla }}={\frac {\partial }{\partial x_{1}}}~\mathbf {e} _{1}+{\frac {\partial }{\partial x_{2}}}~\mathbf {e} _{2}+{\frac {\partial }{\partial x_{3}}}~\mathbf {e} _{3}~.$

### Divergence of a vector field

If we form a scalar product of a vector field $\mathbf {u} (\mathbf {x} )\,$ with the ${\boldsymbol {\nabla }}$ operator, we get a scalar quantity called the divergence of the vector field. Thus,
    ${\boldsymbol {\nabla }}\cdot \mathbf {u} ={\frac {\partial u_{1}}{\partial x_{1}}}+{\frac {\partial u_{2}}{\partial x_{2}}}+{\frac {\partial u_{3}}{\partial x_{3}}}~.$
In index notation,
    ${\boldsymbol {\nabla }}\mathbf {u} \equiv u_{i,i}~.$
If ${\boldsymbol {\nabla }}\cdot \mathbf {u} =0$, then $\mathbf {u} \,$ is called a divergence-free field.
The physical significance of the divergence of a vector field is the rate at which some density exits a given region of space. In the absence of the creation or destruction of matter, the density within a region of space can change only by having it flow into or out of the region.

### Curl of a vector field

The curl of a vector field $\mathbf {u} (\mathbf {x} )\,$ is a vector whose expression can be obtained with
    ${\boldsymbol {\nabla }}\times {\mathbf {u} }={\begin{vmatrix}\mathbf {e} _{1}&\mathbf {e} _{2}&\mathbf {e} _{3}\\{\frac {\partial }{\partial x_{1}}}&{\frac {\partial }{\partial x_{2}}}&{\frac {\partial }{\partial x_{3}}}\\u_{1}&u_{2}&u_{3}\\\end{vmatrix}}$
The physical significance of the curl of a vector field is the amount of rotation or angular momentum of the contents of a region of space.

### Laplacian of a scalar or vector field

The Laplacian of a scalar field $\varphi (\mathbf {x} )\,$ is a scalar defined as
    $\nabla ^{2}{\varphi }:={\boldsymbol {\nabla }}({\boldsymbol {\nabla }}{\varphi })={\frac {\partial ^{2}\varphi }{\partial x_{1}}}+{\frac {\partial ^{2}\varphi }{\partial x_{2}}}+{\frac {\partial ^{2}\varphi }{\partial x_{3}}}~.$
The Laplacian of a vector field $\mathbf {u} (\mathbf {x} )\,$ is a vector defined as
    $\nabla ^{2}{\mathbf {u} }:=(\nabla ^{2}{u_{1}})\mathbf {e} _{1}+(\nabla ^{2}{u_{2}})\mathbf {e} _{2}+(\nabla ^{2}{u_{3}})\mathbf {e} _{3}~.$

### Identities in vector calculus

Some frequently used identities from vector calculus are listed below.
* ${\boldsymbol {\nabla }}(\mathbf {a} +\mathbf {b} )={\boldsymbol {\nabla }}\cdot {\mathbf {a} }+{\boldsymbol {\nabla }}\cdot {\mathbf {b} }$
* ${\boldsymbol {\nabla }}\times {(\mathbf {a} +\mathbf {b} )}={\boldsymbol {\nabla }}\times {\mathbf {a} }+{\boldsymbol {\nabla }}\times {\mathbf {b} }$
* ${\boldsymbol {\nabla }}(\varphi \mathbf {a} )=\cdot {({\boldsymbol {\nabla }}{\varphi })}{\mathbf {a} }+\varphi ({\boldsymbol {\nabla }}\cdot {\mathbf {a} })$
* ${\boldsymbol {\nabla }}\times {(\varphi \mathbf {a} )}={({\boldsymbol {\nabla }}{\varphi })}\times {\mathbf {a} }+\varphi ({\boldsymbol {\nabla }}\times {\mathbf {a} })$
* ${\boldsymbol {\nabla }}({\mathbf {a} }\times {\mathbf {b} })={\mathbf {b} }\cdot {({\boldsymbol {\nabla }}\times {\mathbf {a} })}-{\mathbf {a} }\cdot {({\boldsymbol {\nabla }}\times {\mathbf {b} })}$

## Fundamental theorems of vector calculus

One version of the fundamental theorem of one-dimensional calculus is
    $\int _{a}^{b}f\,'(x)dx=f(b)-f(a)$
This is a theorem about a function, $f(x)$, its first derivative, and a line segment. Two notations used to denote this line segment are [a,b] and the inequality, a<x<b. In the field of topology, $\partial$ denotes boundary. If we let the symbol ${\mathcal {L}}$ denote the infinite number of points in the line segment [a,b], then the symbol $\partial {\mathcal {L}}$ denotes the two endpoints (at x = a and x = b ) of the line segment ${\mathcal {L}}$. These endpoints form the boundary of ${\mathcal {L}}$.

### Gradient theorem

The gradient theorem is a direct generalization of the fundamental theorem of calculus:
    $\int _{\ell [{\vec {p}}\to {\vec {q}}]\subset \mathbb {R} ^{n}}{\vec {\nabla }}f\cdot d{\vec {\ell }}=f\left({\vec {q}}\right)-f\left({\vec {p}}\right)$
The subscript, $\ell [{\vec {p}}\to {\vec {q}}]\subset \mathbb {R} ^{n}$ informs this is an integral over the over a one-dimensinal curve (or 'path') line integral $\ell$ from point ${\vec {r}}={\vec {p}}$ to point ${\vec {r}}={\vec {q}}$. The function, $f=f({\vec {r}})$ is any scalar field that is differentiable. The expression $\subset \mathbb {R} ^{n}$ informs us that ${\vec {r}}$ can be a member of an n-dimensional space. (In other words the theorem is easily generalized to more than three dimensions.) A consequence of this theorem is that $\int {\vec {\nabla }}f\cdot d{\vec {\ell }}=0$ for any "closed curve" The figure shows the closed curve A, as well as the "open curve", B. Two endpoints form the "boundary" of curve B.

### Stokes' theorem

Stokes' theorem states:
    $\int _{\Sigma \subset \mathbb {R} ^{3}}{\vec {\nabla }}\times {\vec {F}}\cdot {\vec {dA}}=\oint _{\partial \Sigma }{\vec {F}}\cdot d{\hat {\ell }}$
The integral subscript, $\Sigma \subset \mathbb {R} ^{3}$ informs us that this theorem is valid only in a three-dimensional vector space. The integral is over a two-dimensional surface,Σ ,with ${\vec {dA}}={\hat {n}}dA$, where ${\hat {n}}$ is normal to the surface. The integral over the surface, Σ, is nonzero only if its boundary, ∂Σ, exists. Surfaces with such boundaries are called open surfaces, and the boundary, ∂Σ, is a curve in 3-space that goes along the "edge" of the surface. This curve is integrated in the direction of positive orientation, meaning that ${\vec {d\ell }}$ and the surface normal follow ${\hat {n}}dA$ follow the right-hand rule.
    Footnote: According to Wikipedia, this form of the theorem was first discovered by Lord Kelvin, who communicated it to George Stokes in a letter dated July 2, 1850. Stokes set the theorem as a question on the 1854 Smith's Prize exam, which led to the result bearing his name.

### Divergence theorem

The divergence theorem states:
    $\int _{\Omega }{\vec {\nabla }}\cdot {\vec {F}}\;dV=\oint _{\partial \Omega \subset \mathbb {R} ^{3}}{\vec {F}}\cdot d{\vec {A}}$.
The integral subscript, $\Omega \subset \mathbb {R} ^{n}$, informs us that this theorem is valid in an (arbitrary) n-dimensional vector space. The n-dimensional volume is Ω, and ∂Ω is its boundary. If n =3 dimensions, ∂Ω is a surface. Since this surface encloses a volume, it has no boundary of its own, and is therefore called a closed surface. The figure shows six surfaces. The three on the left have no boundary and are therefore closed; the ones to the right have a boundary (shown in red) and are therefore open. Note that the closed surfaces to the left are themselves boundaries volumes which are defined as what is "inside" the surface.
    Footnote: In index notation, the gradient theorem can be written as $\int _{\Omega }u_{i,i}~dV=\int _{\partial \Omega }n_{i}u_{i}~dA$
* ↑ https://en.wikiversity.org/w/index.php?title=Vectors&oldid=1194637
* ↑ https://en.wikipedia.org/w/index.php?title=Stokes%27_theorem&oldid=608564969