fractional.operators: Rational approximations of fractional operators

In rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations

Rational approximations of fractional operators

Description

fractional.operators is used for computing an approximation, which can be used for inference and simulation, of the fractional SPDE

L^\beta (\tau u(s)) = W.

Here L is a differential operator, \beta>0 is the fractional power, \tau is a positive scalar or vector that scales the variance of the solution u, and W is white noise.

Usage

fractional.operators(L, beta, C, scale.factor, m = 1, tau = 1)

Arguments

L	A finite element discretization of the operator L.
beta	The positive fractional power.
C	The mass matrix of the finite element discretization.
scale.factor	A constant c is a lower bound for the the smallest eigenvalue of the non-discretized operator L.
m	The order of the rational approximation, which needs to be a positive integer. The default value is 1. Higer values gives a more accurate approximation, which are more computationally expensive to use for inference. Currently, the largest value of m that is implemented is 4.
tau	The constant or vector that scales the variance of the solution. The default value is 1.

Details

The approximation is based on a rational approximation of the fractional operator, resulting in an approximate model on the form

P_l u(s) = P_r W,

where P_j = p_j(L) are non-fractional operators defined in terms of polynomials p_j for j=l,r. The order of p_r is given by m and the order of p_l is m + m_\beta where m_\beta is the integer part of \beta if \beta>1 and m_\beta = 1 otherwise.

The discrete approximation can be written as u = P_r x where x \sim N(0,Q^{-1}) and Q = P_l^T C^{-1} P_l. Note that the matrices P_r and Q may be be ill-conditioned for m>1. In this case, the methods in operator.operations() should be used for operations involving the matrices, since these methods are more numerically stable.

Value

fractional.operators returns an object of class "rSPDEobj". This object contains the following quantities:

Pl	The operator P_l.
Pr	The operator P_r.
C	The mass lumped mass matrix.
Ci	The inverse of C.
m	The order of the rational approximation.
beta	The fractional power.
type	String indicating the type of approximation.
Q	The matrix t(Pl) %*% solve(C,Pl).
type	String indicating the type of approximation.
Pl.factors	List with elements that can be used to assemble P_l.
Pr.factors	List with elements that can be used to assemble P_r.

See Also

matern.operators(), spde.matern.operators(), matern.operators()

Examples

# Compute rational approximation of a Gaussian process with a
# Matern covariance function on R
kappa <- 10
sigma <- 1
nu <- 0.8

# create mass and stiffness matrices for a FEM discretization
x <- seq(from = 0, to = 1, length.out = 101)
fem <- rSPDE.fem1d(x)

# compute rational approximation of covariance function at 0.5
tau <- sqrt(gamma(nu) / (sigma^2 * kappa^(2 * nu) *
  (4 * pi)^(1 / 2) * gamma(nu + 1 / 2)))
op <- fractional.operators(
  L = fem$G + kappa^2 * fem$C, beta = (nu + 1 / 2) / 2,
  C = fem$C, scale.factor = kappa^2, tau = tau
)

v <- t(rSPDE.A1d(x, 0.5))
c.approx <- Sigma.mult(op, v)

# plot the result and compare with the true Matern covariance
plot(x, matern.covariance(abs(x - 0.5), kappa, nu, sigma),
  type = "l", ylab = "C(h)",
  xlab = "h", main = "Matern covariance and rational approximations"
)
lines(x, c.approx, col = 2)

rSPDE documentation built on Nov. 6, 2023, 1:06 a.m.