curl: Numerical and Symbolic Curl

In eguidotti/calculus: High Dimensional Numerical and Symbolic Calculus

Numerical and Symbolic Curl

Description

Computes the numerical curl of functions or the symbolic curl of characters in arbitrary orthogonal coordinate systems.

Usage

curl(
  f,
  var,
  params = list(),
  coordinates = "cartesian",
  accuracy = 4,
  stepsize = NULL,
  drop = TRUE
)

f %curl% var

Arguments

f	array of characters or a function returning a numeric array.
var	vector giving the variable names with respect to which the derivatives are to be computed and/or the point where the derivatives are to be evaluated. See derivative.
params	list of additional parameters passed to f.
coordinates	coordinate system to use. One of: cartesian, polar, spherical, cylindrical, parabolic, parabolic-cylindrical or a vector of scale factors for each varibale.
accuracy	degree of accuracy for numerical derivatives.
stepsize	finite differences stepsize for numerical derivatives. It is based on the precision of the machine by default.
drop	if TRUE, return the curl as a vector and not as an array for vector-valued functions.

Details

The curl of a vector-valued function F_i at a point is represented by a vector whose length and direction denote the magnitude and axis of the maximum circulation. In 2 dimensions, the curl is computed in arbitrary orthogonal coordinate systems using the scale factors h_i and the Levi-Civita symbol epsilon:

\nabla \times F = \frac{1}{h_1h_2}∑_{ij}ε_{ij}\partial_i\Bigl(h_jF_j\Bigl)= \frac{1}{h_1h_2}\Biggl(\partial_1\Bigl(h_2F_2\Bigl)-\partial_2\Bigl(h_1F_1\Bigl)\Biggl)

In 3 dimensions:

(\nabla \times F)_k = \frac{h_k}{J}∑_{ij}ε_{ijk}\partial_i\Bigl(h_jF_j\Bigl)

where J=∏_i h_i. In m+2 dimensions, the curl is implemented in such a way that the formula reduces correctly to the previous cases for m=0 and m=1:

(\nabla \times F)_{k_1… k_m} = \frac{h_{k_1}\cdots h_{k_m}}{J}∑_{ij}ε_{ijk_1… k_m}\partial_i\Bigl(h_jF_j\Bigl)

When F is an array of vector-valued functions F_{d_1,…,d_n,j} the curl is computed for each vector:

(\nabla \times F)_{d_1… d_n,k_1… k_m} = \frac{h_{k_1}\cdots h_{k_m}}{J}∑_{ij}ε_{ijk_1… k_m}\partial_i\Bigl(h_jF_{d_1… d_n,j}\Bigl)

Value

Vector for vector-valued functions when drop=TRUE, array otherwise.

Functions

• f %curl% var: binary operator with default parameters.

References

Guidotti E (2022). "calculus: High-Dimensional Numerical and Symbolic Calculus in R." Journal of Statistical Software, 104(5), 1-37. doi: 10.18637/jss.v104.i05

See Also

Other differential operators: derivative(), divergence(), gradient(), hessian(), jacobian(), laplacian()

Examples

### symbolic curl of a 2-d vector field
f <- c("x^3*y^2","x")
curl(f, var = c("x","y"))

### numerical curl of a 2-d vector field in (x=1, y=1)
f <- function(x,y) c(x^3*y^2, x)
curl(f, var = c(x=1, y=1))

### numerical curl of a 3-d vector field in (x=1, y=1, z=1)
f <- function(x,y,z) c(x^3*y^2, x, z)
curl(f, var = c(x=1, y=1, z=1))

### vectorized interface
f <- function(x) c(x[1]^3*x[2]^2, x[1], x[3])
curl(f, var = c(1,1,1)) 

### symbolic array of vector-valued 3-d functions
f <- array(c("x*y","x","y*z","y","x*z","z"), dim = c(2,3))
curl(f, var = c("x","y","z"))

### numeric array of vector-valued 3-d functions in (x=1, y=1, z=1)
f <- function(x,y,z) array(c(x*y,x,y*z,y,x*z,z), dim = c(2,3))
curl(f, var = c(x=1, y=1, z=1))

### binary operator
c("x*y","y*z","x*z") %curl% c("x","y","z")

eguidotti/calculus documentation built on March 17, 2023, 5:18 a.m.