divergence: Numerical and Symbolic Divergence

In eguidotti/calculus: High Dimensional Numerical and Symbolic Calculus

Numerical and Symbolic Divergence

Description

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

Usage

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

f %divergence% 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 divergence as a scalar and not as an array for vector-valued functions.

Details

The divergence of a vector-valued function F_i produces a scalar value \nabla \cdot F representing the volume density of the outward flux of the vector field from an infinitesimal volume around a given point. The divergence is computed in arbitrary orthogonal coordinate systems using the scale factors h_i:

\nabla \cdot F = \frac{1}{J}∑_i\partial_i\Biggl(\frac{J}{h_i}F_i\Biggl)

where J=∏_ih_i. When F is an array of vector-valued functions F_{d_1… d_n,i}, the divergence is computed for each vector:

(\nabla \cdot F)_{d_1… d_n} = \frac{1}{J}∑_i\partial_i\Biggl(\frac{J}{h_i}F_{d_1… d_n,i}\Biggl)

Value

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

Functions

• f %divergence% 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: curl(), derivative(), gradient(), hessian(), jacobian(), laplacian()

Examples

### symbolic divergence of a vector field
f <- c("x^2","y^3","z^4")
divergence(f, var = c("x","y","z"))

### numerical divergence of a vector field in (x=1, y=1, z=1)
f <- function(x,y,z) c(x^2, y^3, z^4)
divergence(f, var = c(x=1, y=1, z=1))

### vectorized interface
f <- function(x) c(x[1]^2, x[2]^3, x[3]^4)
divergence(f, var = c(1,1,1)) 

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

### numeric array of vector-valued 3-d functions in (x=0, y=0, z=0)
f <- function(x,y,z) array(c(x^2,x,y^2,y,z^2,z), dim = c(2,3))
divergence(f, var = c(x=0, y=0, z=0))
 
### binary operator
c("x^2","y^3","z^4") %divergence% c("x","y","z")

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