Vector calculus identities

The following are important identities involving derivatives and integrals in vector calculus.

Gradient
For a function $$f(x, y, z)$$ in three-dimensional Cartesian coordinate variables, the gradient is the vector field:

\operatorname{grad}(f) = \nabla f = \begin{pmatrix}\displaystyle \frac{\partial }{\partial x},\ \frac{\partial }{\partial y},\ \frac{\partial }{\partial z} \end{pmatrix} f = \frac{\partial f}{\partial x} \mathbf{i} + \frac{\partial f}{\partial y} \mathbf{j} + \frac{\partial f}{\partial z} \mathbf{k} $$ where i, j, k are the standard unit vectors for the x, y, z-axes. More generally, for a function of n variables $$\psi(x_1, \ldots, x_n)$$, also called a scalar field, the gradient is the vector field: $$ \nabla\psi = \begin{pmatrix}\displaystyle\frac{\partial}{\partial x_1}, \ldots, \frac{\partial}{\partial x_n} \end{pmatrix}\psi = \frac{\partial\psi}{\partial x_1} \mathbf{e}_1 + \dots + \frac{\partial\psi}{\partial x_n}\mathbf{e}_n $$ where $$\mathbf{e}_{i} \, (i=1,2,..., n)$$ are mutually orthogonal unit vectors.

As the name implies, the gradient is proportional to, and points in the direction of, the function's most rapid (positive) change.

For a vector field $$\mathbf{A} = \left(A_1, \ldots, A_n\right)$$, also called a tensor field of order 1, the gradient or total derivative is the n × n Jacobian matrix: $$ \mathbf{J}_{\mathbf{A}} = d\mathbf{A} = (\nabla \!\mathbf{A})^\textsf{T} = \left(\frac{\partial A_i}{\partial x_j}\right)_{\!ij}.$$

For a tensor field $$\mathbf{T}$$ of any order k, the gradient $$\operatorname{grad}(\mathbf{T}) = d\mathbf{T} = (\nabla \mathbf{T})^\textsf{T}$$ is a tensor field of order k + 1.

For a tensor field $$ \mathbf{T} $$ of order k > 0, the tensor field $$ \nabla \mathbf{T} $$ of order k + 1 is defined by the recursive relation $$ (\nabla \mathbf{T}) \cdot \mathbf{C} = \nabla (\mathbf{T} \cdot \mathbf{C}) $$ where $$ \mathbf{C} $$ is an arbitrary constant vector.

Divergence
In Cartesian coordinates, the divergence of a continuously differentiable vector field $$\mathbf{F} = F_x\mathbf{i} + F_y\mathbf{j} + F_z\mathbf{k}$$ is the scalar-valued function: $$ \operatorname{div}\mathbf{F} = \nabla\cdot\mathbf{F} = \begin{pmatrix}\displaystyle\frac{\partial}{\partial x},\ \frac{\partial}{\partial y},\ \frac{\partial}{\partial z}\end{pmatrix} \cdot \begin{pmatrix}F_{x},\ F_{y},\ F_{z}\end{pmatrix} = \frac{\partial F_x}{\partial x} + \frac{\partial F_y}{\partial y} + \frac{\partial F_z}{\partial z}. $$

As the name implies, the divergence is a (local) measure of the degree to which vectors in the field diverge.

The divergence of a tensor field $$\mathbf{T}$$ of non-zero order k is written as $$\operatorname{div}(\mathbf{T}) = \nabla \cdot \mathbf{T}$$, a contraction of a tensor field of order k − 1. Specifically, the divergence of a vector is a scalar. The divergence of a higher-order tensor field may be found by decomposing the tensor field into a sum of outer products and using the identity, $$\nabla \cdot \left(\mathbf{A} \otimes \mathbf{T}\right) = \mathbf{T} (\nabla \cdot \mathbf{A}) + (\mathbf{A} \cdot \nabla) \mathbf{T}$$ where $$\mathbf{A} \cdot \nabla$$ is the directional derivative in the direction of $$\mathbf{A}$$ multiplied by its magnitude. Specifically, for the outer product of two vectors, $$\nabla \cdot \left(\mathbf{A} \mathbf{B}^\textsf{T}\right) = \mathbf{B} (\nabla \cdot \mathbf{A}) + (\mathbf{A} \cdot \nabla) \mathbf{B}.$$

For a tensor field $$ \mathbf{T} $$ of order k > 1, the tensor field $$ \nabla \cdot \mathbf{T} $$ of order k − 1 is defined by the recursive relation $$ (\nabla \cdot \mathbf{T}) \cdot \mathbf{C} = \nabla \cdot (\mathbf{T} \cdot \mathbf{C}) $$ where $$ \mathbf{C} $$ is an arbitrary constant vector.

Curl
In Cartesian coordinates, for $$\mathbf{F} = F_x\mathbf{i} + F_y\mathbf{j} + F_z\mathbf{k}$$ the curl is the vector field: $$ \begin{align} \operatorname{curl}\mathbf{F} &= \nabla \times \mathbf{F} = \begin{pmatrix}\displaystyle\frac{\partial}{\partial x},\ \frac{\partial}{\partial y},\ \frac{\partial}{\partial z}\end{pmatrix} \times \begin{pmatrix}F_{x},\ F_{y},\ F_{z}\end{pmatrix} =   \begin{vmatrix} \mathbf{i} & \mathbf{j} & \mathbf{k} \\ \frac{\partial}{\partial x} & \frac{\partial}{\partial y} & \frac{\partial}{\partial z} \\ F_x & F_y & F_z \end{vmatrix} \\[1em] &=   \left(\frac{\partial F_z}{\partial y} - \frac{\partial F_y}{\partial z}\right) \mathbf{i} + \left(\frac{\partial F_x}{\partial z} - \frac{\partial F_z}{\partial x}\right) \mathbf{j} + \left(\frac{\partial F_y}{\partial x} - \frac{\partial F_x}{\partial y}\right) \mathbf{k} \end{align}$$ where i, j, and k are the unit vectors for the x-, y-, and z-axes, respectively.

As the name implies the curl is a measure of how much nearby vectors tend in a circular direction.

In Einstein notation, the vector field $$\mathbf{F} = \begin{pmatrix}F_1,\ F_2,\ F_3\end{pmatrix}$$ has curl given by: $$\nabla \times \mathbf{F} = \varepsilon^{ijk}\mathbf{e}_i \frac{\partial F_k}{\partial x_j}$$ where $$\varepsilon$$ = ±1 or 0 is the Levi-Civita parity symbol.

For a tensor field $$ \mathbf{T} $$ of order k > 1, the tensor field $$ \nabla \times \mathbf{T} $$ of order k is defined by the recursive relation $$ (\nabla \times \mathbf{T}) \cdot \mathbf{C} = \nabla \times (\mathbf{T} \cdot \mathbf{C}) $$ where $$ \mathbf{C} $$ is an arbitrary constant vector.

A tensor field of order greater than one may be decomposed into a sum of outer products, and then the following identity may be used: $$\nabla \times \left(\mathbf{A} \otimes \mathbf{T}\right) = (\nabla \times \mathbf{A}) \otimes \mathbf{T} - \mathbf{A} \times (\nabla \mathbf{T}).$$ Specifically, for the outer product of two vectors, $$\nabla \times \left(\mathbf{A} \mathbf{B}^\textsf{T}\right) = (\nabla \times \mathbf{A}) \mathbf{B}^\textsf{T} - \mathbf{A} \times (\nabla \mathbf{B}).$$

Laplacian
In Cartesian coordinates, the Laplacian of a function $$f(x,y,z)$$ is $$\Delta f = \nabla^2\! f = (\nabla \cdot \nabla) f = \frac{\partial^2\! f}{\partial x^2} + \frac{\partial^2\! f}{\partial y^2} + \frac{\partial^2\! f}{\partial z^2}.$$

The Laplacian is a measure of how much a function is changing over a small sphere centered at the point.

When the Laplacian is equal to 0, the function is called a harmonic function. That is, $$\Delta f = 0.$$

For a tensor field, $$\mathbf{T}$$, the Laplacian is generally written as: $$\Delta\mathbf{T} = \nabla^2 \mathbf{T} = (\nabla \cdot \nabla) \mathbf{T}$$ and is a tensor field of the same order.

For a tensor field $$ \mathbf{T} $$ of order k > 0, the tensor field $$ \nabla^2 \mathbf{T} $$ of order k is defined by the recursive relation $$ \left(\nabla^2 \mathbf{T}\right) \cdot \mathbf{C} = \nabla^2 (\mathbf{T} \cdot \mathbf{C}) $$ where $$ \mathbf{C} $$ is an arbitrary constant vector.

Special notations
In Feynman subscript notation, $$\nabla_\mathbf{B}\! \left( \mathbf{A {\cdot} B} \right) = \mathbf{A} {\times}\! \left( \nabla {\times} \mathbf{B} \right) + \left( \mathbf{A} {\cdot} \nabla \right) \mathbf{B}$$ where the notation ∇B means the subscripted gradient operates on only the factor B.

Less general but similar is the Hestenes overdot notation in geometric algebra. The above identity is then expressed as: $$\dot{\nabla} \left( \mathbf{A} {\cdot} \dot{\mathbf{B}} \right) = \mathbf{A} {\times}\! \left( \nabla {\times} \mathbf{B} \right) + \left( \mathbf{A} {\cdot} \nabla \right) \mathbf{B} $$ where overdots define the scope of the vector derivative. The dotted vector, in this case B, is differentiated, while the (undotted) A is held constant.

For the remainder of this article, Feynman subscript notation will be used where appropriate.

First derivative identities
For scalar fields $$\psi$$, $$\phi$$ and vector fields $$\mathbf{A}$$, $$\mathbf{B}$$, we have the following derivative identities.

Distributive properties

 * $$\begin{align}

\nabla ( \psi + \phi ) &= \nabla \psi + \nabla \phi \\ \nabla ( \mathbf{A} + \mathbf{B} ) &= \nabla \mathbf{A} + \nabla \mathbf{B} \\ \nabla \cdot ( \mathbf{A} + \mathbf{B} ) &= \nabla \cdot \mathbf{A} + \nabla \cdot \mathbf{B} \\ \nabla \times ( \mathbf{A} + \mathbf{B} ) &= \nabla \times \mathbf{A} + \nabla \times \mathbf{B} \end{align}$$

First derivative associative properties

 * $$\begin{align}

( \mathbf{A} \cdot \nabla ) \psi &= \mathbf{A} \cdot ( \nabla \psi ) \\ ( \mathbf{A} \cdot \nabla ) \mathbf{B} &= \mathbf{A} \cdot ( \nabla \mathbf{B} ) \\ ( \mathbf{A} \times \nabla ) \psi &= \mathbf{A} \times ( \nabla \psi ) \\ ( \mathbf{A} \times \nabla ) \mathbf{B} &= \mathbf{A} \times ( \nabla \mathbf{B} ) \end{align}$$

Product rule for multiplication by a scalar
We have the following generalizations of the product rule in single-variable calculus.


 * $$\begin{align}

\nabla ( \psi \phi ) &= \phi\, \nabla \psi + \psi\, \nabla \phi \\ \nabla ( \psi \mathbf{A} ) &= (\nabla \psi) \mathbf{A}^\textsf{T} + \psi \nabla \mathbf{A} \ =\ \nabla \psi \otimes \mathbf{A} + \psi\, \nabla \mathbf{A} \\ \nabla \cdot ( \psi \mathbf{A} ) &= \psi\, \nabla {\cdot} \mathbf{A} + ( \nabla \psi ) \,{\cdot} \mathbf{A} \\ \nabla {\times} ( \psi \mathbf{A} ) &= \psi\, \nabla {\times} \mathbf{A} + ( \nabla \psi ) {\times} \mathbf{A} \\ \nabla^{2}(\psi \phi) &= \psi\,\nabla^{2\!}\phi + 2\,\nabla\! \psi\cdot\!\nabla \phi+\phi\, \nabla^{2\!}\psi \end{align}$$

Quotient rule for division by a scalar

 * $$\begin{align}

\nabla\left(\frac{\psi}{\phi}\right) &= \frac{\phi\,\nabla \psi - \psi\,\nabla\phi}{\phi^2} \\[1em] \nabla\left(\frac{\mathbf{A}}{\phi}\right) &= \frac{\phi\,\nabla \mathbf{A} - \nabla\phi \otimes \mathbf{A}}{\phi^2} \\[1em] \nabla \cdot \left(\frac{\mathbf{A}}{\phi}\right) &= \frac{\phi\, \nabla{\cdot} \mathbf{A} - \nabla\!\phi \cdot \mathbf{A}}{\phi^2} \\[1em] \nabla \times \left(\frac{\mathbf{A}}{\phi}\right) &= \frac{\phi\, \nabla {\times} \mathbf{A} - \nabla\!\phi \,{\times}\, \mathbf{A}}{\phi^2} \\[1em] \nabla^2 \left(\frac{\psi}{\phi}\right) &= \frac{\phi\, \nabla^{2\!}\psi - 2\, \phi\, \nabla\!\left(\frac{\psi}{\phi}\right)\cdot\!\nabla\phi - \psi\, \nabla^{2\!}\phi}{\phi^2} \end{align}$$

Chain rule
Let $$f(x)$$ be a one-variable function from scalars to scalars, $$\mathbf{r}(t) = (x_1(t), \ldots, x_n(t))$$ a parametrized curve, $$\phi\!: \mathbb{R}^n \to \mathbb{R}$$ a function from vectors to scalars, and $$\mathbf{A}\!: \mathbb{R}^n \to \mathbb{R}^n$$ a vector field. We have the following special cases of the multi-variable chain rule.


 * $$\begin{align}

\nabla(f \circ \phi) &= \left(f' \circ \phi\right) \nabla \phi \\ (\mathbf{r} \circ f)' &= (\mathbf{r}' \circ f) f' \\ (\phi \circ \mathbf{r})' &= (\nabla \phi \circ \mathbf{r}) \cdot \mathbf{r}' \\ (\mathbf{A} \circ \mathbf{r})' &= \mathbf{r}' \cdot (\nabla \mathbf{A} \circ \mathbf{r}) \\ \nabla(\phi \circ \mathbf{A}) &= (\nabla \mathbf{A}) \cdot (\nabla \phi \circ \mathbf{A}) \\ \nabla \cdot (\mathbf{r} \circ \phi) &= \nabla \phi \cdot (\mathbf{r}' \circ \phi) \\ \nabla \times (\mathbf{r} \circ \phi) &= \nabla \phi \times (\mathbf{r}' \circ \phi) \end{align}$$

For a vector transformation $$\mathbf{x}\!: \mathbb{R}^n \to \mathbb{R}^n$$ we have:


 * $$\nabla \cdot (\mathbf{A} \circ \mathbf{x}) = \mathrm{tr} \left((\nabla \mathbf{x}) \cdot (\nabla \mathbf{A} \circ \mathbf{x})\right)$$

Here we take the trace of the dot product of two second-order tensors, which corresponds to the product of their matrices.

Dot product rule

 * $$\begin{align}

\nabla(\mathbf{A} \cdot \mathbf{B}) &\ =\ (\mathbf{A} \cdot \nabla)\mathbf{B} \,+\, (\mathbf{B} \cdot \nabla)\mathbf{A} \,+\,  \mathbf{A} {\times} (\nabla {\times} \mathbf{B}) \,+\,  \mathbf{B} {\times} (\nabla {\times} \mathbf{A}) \\ &\ =\ \mathbf{A}\cdot\mathbf{J}_\mathbf{B} + \mathbf{B}\cdot\mathbf{J}_\mathbf{A} \ =\ (\nabla\mathbf{B})\cdot \mathbf{A} \,+\, (\nabla\mathbf{A}) \cdot\mathbf{B} \end{align}$$

where $$\mathbf{J}_{\mathbf{A}} = (\nabla \!\mathbf{A})^\textsf{T} = (\partial A_i/\partial x_j)_{ij}$$ denotes the Jacobian matrix of the vector field $$\mathbf{A} = (A_1,\ldots,A_n)$$.

Alternatively, using Feynman subscript notation,


 * $$ \nabla(\mathbf{A} \cdot \mathbf{B}) = \nabla_\mathbf{A}(\mathbf{A} \cdot \mathbf{B}) +  \nabla_\mathbf{B} (\mathbf{A} \cdot \mathbf{B}) \ . $$

See these notes.

As a special case, when $A = B$,



\tfrac{1}{2} \nabla \left( \mathbf{A} \cdot \mathbf{A} \right) \ =\ \mathbf{A} \cdot \mathbf{J}_\mathbf{A} \ =\ (\nabla \mathbf{A})\cdot \mathbf{A}\ =\ (\mathbf{A} {\cdot} \nabla) \mathbf{A} \,+\, \mathbf{A} {\times} (\nabla {\times} \mathbf{A}) \ =\ A \nabla A. $$

The generalization of the dot product formula to Riemannian manifolds is a defining property of a Riemannian connection, which differentiates a vector field to give a vector-valued 1-form.

Cross product rule

 * $$\begin{align}

\nabla \cdot (\mathbf{A} \times \mathbf{B}) &\ =\ (\nabla {\times} \mathbf{A}) \cdot \mathbf{B} \,-\, \mathbf{A} \cdot (\nabla {\times} \mathbf{B}) \\[5pt] \nabla \times (\mathbf{A} \times \mathbf{B}) &\ =\ \mathbf{A}(\nabla {\cdot} \mathbf{B}) \,-\, \mathbf{B}(\nabla {\cdot} \mathbf{A}) \,+\, (\mathbf{B} {\cdot} \nabla) \mathbf{A} \,-\, (\mathbf{A} {\cdot} \nabla) \mathbf{B} \\[2pt] &\ =\ \mathbf{A}(\nabla {\cdot} \mathbf{B}) \,+\, (\mathbf{B} {\cdot} \nabla) \mathbf{A} \,-\, (\mathbf{B}(\nabla {\cdot} \mathbf{A}) \,+\, (\mathbf{A} {\cdot} \nabla) \mathbf{B}) \\[2pt] &\ =\ \nabla {\cdot} \left(\mathbf{B} \mathbf{A}^\textsf{T}\right) \,-\, \nabla {\cdot} \left(\mathbf{A} \mathbf{B}^\textsf{T}\right) \\[2pt] &\ =\ \nabla {\cdot} \left(\mathbf{B} \mathbf{A}^\textsf{T} \,-\, \mathbf{A} \mathbf{B}^\textsf{T}\right) \\[5pt] \mathbf{A} \times (\nabla \times \mathbf{B}) &\ =\ \nabla_{\mathbf{B}}(\mathbf{A} {\cdot} \mathbf{B}) \,-\, (\mathbf{A} {\cdot} \nabla) \mathbf{B} \\[2pt] &\ =\ \mathbf{A} \cdot \mathbf{J}_\mathbf{B} \,-\, (\mathbf{A} {\cdot} \nabla) \mathbf{B} \\[2pt] &\ =\ (\nabla\mathbf{B})\cdot\mathbf{A} \,-\, \mathbf{A} \cdot (\nabla \mathbf{B}) \\[2pt] &\ =\ \mathbf{A} \cdot (\mathbf{J}_\mathbf{B} \,-\, \mathbf{J}_\mathbf{B}^\textsf{T}) \\[5pt] (\mathbf{A} \times \nabla) \times \mathbf{B} &\ =\ (\nabla\mathbf{B}) \cdot \mathbf{A} \,-\, \mathbf{A}  (\nabla {\cdot} \mathbf{B}) \\[2pt] &\ =\ \mathbf{A} \times (\nabla \times \mathbf{B}) \,+\, (\mathbf{A} {\cdot} \nabla) \mathbf{B} \,-\, \mathbf{A} (\nabla {\cdot} \mathbf{B}) \\[5pt] (\mathbf{A} \times \nabla) \cdot \mathbf{B} &\ =\ \mathbf{A} \cdot (\nabla {\times} \mathbf{B}) \end{align}$$

Note that the matrix $$ \mathbf{J}_\mathbf{B} \,-\, \mathbf{J}_\mathbf{B}^\textsf{T}$$ is antisymmetric.

Divergence of curl is zero
The divergence of the curl of any continuously twice-differentiable vector field A is always zero: $$\nabla \cdot ( \nabla \times \mathbf{A} ) = 0 $$

This is a special case of the vanishing of the square of the exterior derivative in the De Rham chain complex.

Divergence of gradient is Laplacian
The Laplacian of a scalar field is the divergence of its gradient: $$\Delta \psi = \nabla^2 \psi = \nabla \cdot (\nabla \psi) $$ The result is a scalar quantity.

Divergence of divergence is not defined
The divergence of a vector field A is a scalar, and the divergence of a scalar quantity is undefined. Therefore, $$ \nabla \cdot (\nabla \cdot \mathbf{A}) \text{ is undefined.} $$

Curl of gradient is zero
The curl of the gradient of any continuously twice-differentiable scalar field $$\varphi $$ (i.e., differentiability class $$C^2$$) is always the zero vector: $$\nabla \times ( \nabla \varphi ) = \mathbf{0}.$$

It can be easily proved by expressing $$\nabla \times ( \nabla \varphi )$$ in a Cartesian coordinate system with Schwarz's theorem (also called Clairaut's theorem on equality of mixed partials). This result is a special case of the vanishing of the square of the exterior derivative in the De Rham chain complex.

Curl of curl
$$ \nabla \times \left( \nabla \times \mathbf{A} \right) \ =\ \nabla(\nabla {\cdot} \mathbf{A}) \,-\, \nabla^{2\!}\mathbf{A}$$

Here ∇2 is the vector Laplacian operating on the vector field A.

Curl of divergence is not defined
The divergence of a vector field A is a scalar, and the curl of a scalar quantity is undefined. Therefore, $$ \nabla \times (\nabla \cdot \mathbf{A}) \text{ is undefined.} $$

Second derivative associative properties

 * $$\begin{align}

( \nabla \cdot \nabla ) \psi &= \nabla \cdot ( \nabla \psi ) = \nabla^2 \psi \\ ( \nabla \cdot \nabla ) \mathbf{A} &= \nabla \cdot ( \nabla \mathbf{A} ) = \nabla^2 \mathbf{A} \\ ( \nabla \times \nabla ) \psi &= \nabla \times ( \nabla \psi ) = \mathbf{0} \\ ( \nabla \times \nabla ) \mathbf{A} &= \nabla \times ( \nabla \mathbf{A} ) = \mathbf{0} \end{align}$$

[[File:DCG chart.svg|right|thumb|300px|DCG chart:

Some rules for second derivatives. ]]

A mnemonic
The figure to the right is a mnemonic for some of these identities. The abbreviations used are:
 * D: divergence,
 * C: curl,
 * G: gradient,
 * L: Laplacian,
 * CC: curl of curl.

Each arrow is labeled with the result of an identity, specifically, the result of applying the operator at the arrow's tail to the operator at its head. The blue circle in the middle means curl of curl exists, whereas the other two red circles (dashed) mean that DD and GG do not exist.

Gradient
+ \mathbf{B} \times (\nabla \times \mathbf{A})$$
 * $$\nabla(\psi+\phi)=\nabla\psi+\nabla\phi $$
 * $$\nabla(\psi \phi) = \phi\nabla \psi + \psi \nabla \phi $$
 * $$\nabla(\psi \mathbf{A} ) = \nabla \psi \otimes \mathbf{A} + \psi \nabla \mathbf{A}$$
 * $$\nabla(\mathbf{A} \cdot \mathbf{B}) = (\mathbf{A} \cdot \nabla)\mathbf{B} + (\mathbf{B} \cdot \nabla)\mathbf{A} + \mathbf{A} \times (\nabla \times \mathbf{B})

Divergence

 * $$ \nabla\cdot(\mathbf{A}+\mathbf{B})= \nabla\cdot\mathbf{A}+\nabla\cdot\mathbf{B} $$
 * $$ \nabla\cdot\left(\psi\mathbf{A}\right)= \psi\nabla\cdot\mathbf{A}+\mathbf{A}\cdot\nabla \psi$$
 * $$ \nabla\cdot\left(\mathbf{A}\times\mathbf{B}\right)= (\nabla\times\mathbf{A})\cdot \mathbf{B}-(\nabla\times\mathbf{B})\cdot \mathbf{A}$$

Curl

 * $$\nabla\times(\mathbf{A}+\mathbf{B})=\nabla\times\mathbf{A}+\nabla\times\mathbf{B} $$
 * $$\nabla\times\left(\psi\mathbf{A}\right)=\psi\,(\nabla\times\mathbf{A})-(\mathbf{A}\times\nabla)\psi=\psi\,(\nabla\times\mathbf{A})+(\nabla\psi)\times\mathbf{A}$$
 * $$\nabla\times\left(\psi\nabla\phi\right)= \nabla \psi \times \nabla \phi$$
 * $$\nabla\times\left(\mathbf{A}\times\mathbf{B}\right)= \mathbf{A}\left(\nabla\cdot\mathbf{B}\right)-\mathbf{B} \left( \nabla\cdot\mathbf{A}\right)+\left(\mathbf{B}\cdot\nabla\right)\mathbf{A}- \left(\mathbf{A}\cdot\nabla\right)\mathbf{B} $$

Vector-dot-Del Operator

 * $$(\mathbf{A} \cdot \nabla)\mathbf{B} = \frac{1}{2}\bigg[\nabla(\mathbf{A} \cdot \mathbf{B}) - \nabla\times(\mathbf{A} \times \mathbf{B}) - \mathbf{B}\times(\nabla \times \mathbf{A}) - \mathbf{A}\times(\nabla \times \mathbf{B}) - \mathbf{B}(\nabla \cdot \mathbf{A}) + \mathbf{A}(\nabla \cdot\mathbf{B})\bigg]$$


 * $$(\mathbf{A} \cdot \nabla)\mathbf{A} = \frac{1}{2}\nabla |\mathbf{A}|^2-\mathbf{A}\times(\nabla\times\mathbf{A}) = \frac{1}{2}\nabla |\mathbf{A}|^2 + (\nabla\times\mathbf{A})\times \mathbf{A}$$

Second derivatives

 * $$\nabla \cdot (\nabla \times \mathbf{A}) = 0$$
 * $$\nabla \times (\nabla\psi) = \mathbf{0}$$
 * $$\nabla \cdot (\nabla\psi) = \nabla^2\psi$$       (scalar Laplacian)
 * $$\nabla\left(\nabla \cdot \mathbf{A}\right) - \nabla \times \left(\nabla \times \mathbf{A}\right) = \nabla^2\mathbf{A}$$  (vector Laplacian)
 * $$\nabla \cdot (\phi\nabla\psi) = \phi\nabla^2\psi + \nabla\phi \cdot \nabla\psi$$
 * $$\psi\nabla^2\phi - \phi\nabla^2\psi = \nabla \cdot \left(\psi\nabla\phi - \phi\nabla\psi\right)$$
 * $$\nabla^2(\phi\psi) = \phi\nabla^2\psi + 2(\nabla\phi) \cdot(\nabla\psi) + \left(\nabla^2\phi\right)\psi$$
 * $$\nabla^2(\psi\mathbf{A}) = \mathbf{A}\nabla^2\psi + 2(\nabla\psi \cdot \nabla)\mathbf{A} + \psi\nabla^2\mathbf{A}$$
 * $$\nabla^2(\mathbf{A} \cdot \mathbf{B}) = \mathbf{A} \cdot \nabla^2\mathbf{B} - \mathbf{B} \cdot \nabla^2\!\mathbf{A} + 2\nabla \cdot ((\mathbf{B} \cdot \nabla)\mathbf{A} + \mathbf{B} \times (\nabla \times \mathbf{A}))$$ (Green's vector identity)

Third derivatives

 * $$  \nabla^2(\nabla\psi) =  \nabla(\nabla \cdot (\nabla\psi)) = \nabla\left(\nabla^2\psi\right)$$
 * $$  \nabla^2(\nabla \cdot \mathbf{A}) =  \nabla \cdot (\nabla(\nabla \cdot \mathbf{A})) = \nabla \cdot \left(\nabla^2\mathbf{A}\right)$$
 * $$ \nabla^{2}(\nabla\times\mathbf{A}) = -\nabla \times (\nabla \times (\nabla \times \mathbf{A})) = \nabla \times \left(\nabla^2\mathbf{A}\right)$$

Integration
Below, the ∂|curly symbol ∂ means "boundary of" a surface or solid.

Surface–volume integrals
In the following surface–volume integral theorems, V denotes a three-dimensional volume with a corresponding two-dimensional boundary S = ∂V (a closed surface):


 * (divergence theorem)
 * (Green's first identity)
 * (Green's second identity)
 * (integration by parts)
 * (integration by parts)
 * (integration by parts)
 * (integration by parts)
 * (integration by parts)

Curve–surface integrals
In the following curve–surface integral theorems, S denotes a 2d open surface with a corresponding 1d boundary C = ∂S (a closed curve):


 * $$ \oint_{\partial S}\mathbf{A}\cdot d\boldsymbol{\ell}\ =\ \iint_{S}\left(\nabla \times \mathbf{A}\right)\cdot d\mathbf{S} $$ (Stokes' theorem)
 * $$ \oint_{\partial S}\psi\, d\boldsymbol{\ell}\ =\ -\iint_{S} \nabla\psi \times d\mathbf{S} $$
 * $$ \oint_{\partial S}\mathbf{A}\times d\boldsymbol{\ell}\ =\ -\iint_{S}\left(\nabla \mathbf{A} - (\nabla \cdot \mathbf{A})\mathbf{1}\right)\cdot d\mathbf{S}\ =\ -\iint_{S}\left(d\mathbf{S} \times \nabla\right)\times \mathbf{A} $$

Integration around a closed curve in the clockwise sense is the negative of the same line integral in the counterclockwise sense (analogous to interchanging the limits in a definite integral):

Endpoint-curve integrals
In the following endpoint–curve integral theorems, P denotes a 1d open path with signed 0d boundary points $$\mathbf{q}-\mathbf{p} = \partial P$$ and integration along P is from $$\mathbf{p}$$ to $$\mathbf{q}$$:


 * $$ \psi|_{\partial P} = \psi(\mathbf{q})-\psi(\mathbf{p}) = \int_{P} \nabla\psi\cdot d\boldsymbol{\ell} $$ (gradient theorem)
 * $$ \mathbf{A}|_{\partial P} = \mathbf{A}(\mathbf{q})-\mathbf{A}(\mathbf{p}) = \int_{P} \left(d\boldsymbol{\ell} \cdot \nabla\right)\mathbf{A} $$