The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Type: Package
Title: Multi-Fidelity Emulator for Computer Experiments with Tunable Fidelity Levels
Version: 0.0.1
Date: 2025-08-11
Description: Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, <doi:10.48550/arXiv.2503.23158>).
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2)]
Encoding: UTF-8
Imports: lhs, parallel, methods, Rcpp
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.2
NeedsCompilation: yes
Packaged: 2025-08-26 15:22:26 UTC; rboutelet
Author: Romain Boutelet [aut, cre], Chih-Li Sung [aut]
Maintainer: Romain Boutelet <boutelet@msu.edu>
Repository: CRAN
Date/Publication: 2025-09-01 09:50:16 UTC

IMSPE optimal design point search

Description

Search for the best next design point according to the IMSPE criterion given a current MuFiMeshGP model fit.

Usage

IMSPE_AL(
  object,
  t.min,
  t.max,
  cost.func,
  cost.new = 0,
  gr = FALSE,
  gr_cost.func = NULL,
  DesCand = NULL,
  Wijs = NULL,
  Hijs = NULL,
  control = list(multi.start.n = 20, maxit = 20, DesStart = NULL, seed = NULL, ncores =
    1)
)

Arguments

object

Current MuFiMeshGP model fit.

t.min, t.max

Lower and upper bounds on the fidelity space for the search.

cost.func

Function that maps the tunable parameter t to the corresponding cost running a simulation at that fidelity level. For example, function(t) 1/t^2.

cost.new

(optional) Cost of running a new simulation at a new fidelity level, scalar.

gr

whether the gradient should be used in the optimization of the IMSPE. (Not recommended due to numerical errors)

gr_cost.func

If grad is TRUE, the user needs to specify the gradient of the cost function, as a function.

DesCand

Design candidates to evaluate from.

Wijs, Hijs

(optional) Matrices from previous IMSPE search to obtain faster computation through matrix decomposition.

control

list of arguments udes for the optimization.

Value

a list with:

Examples

# Example code

f <- function(x, t){
  x <- c(x)
  return(exp(-1.4*x)*cos(3.5*pi*x)+sin(40*x)/10*t^2)
}

set.seed(1)
X <- matrix(runif(15,0,1), ncol = 1)
tt <- runif(15,0.5,2)

Y <- f(c(X), tt)

fit.mufimeshgp <- MuFiMeshGP(X, tt, Y)

xx <- matrix(seq(0,1,0.01), ncol = 1)
ftrue <- f(xx, 0)

# predict
pred.mufimeshgp <- predict(fit.mufimeshgp, xx, rep(0,101))

mu <- pred.mufimeshgp$mean
s <- pred.mufimeshgp$sd
lower <- mu + qnorm(0.025)*s
upper <- mu + qnorm(0.975)*s

# plot

oldpar <- par(mfrow = c(1,2))
plot(xx, ftrue, "l", ylim = c(-1,1.3), ylab = "y", xlab = "x")
lines(c(xx), mu, col = "blue")
lines(c(xx), lower, col = "blue", lty = 2)
lines(c(xx), upper, col = "blue", lty = 2)
points(c(X), Y, col = "red")

### RMSE ###
print(sqrt(mean((ftrue - mu))^2))

best <- IMSPE_AL(fit.mufimeshgp, 0.5, 2, function(t) return(1 / t^2))
new.Y <- f(best$x, best$t)
fit.mufimeshgp <- update(fit.mufimeshgp, best$x, best$t, new.Y)

pred.mufimeshgp <- predict(fit.mufimeshgp, xx, rep(0, 101))
mu <- pred.mufimeshgp$mean
s <- pred.mufimeshgp$sd
lower <- mu + qnorm(0.025)*s
upper <- mu + qnorm(0.975)*s

plot(xx, ftrue, "l", ylim = c(-1,1.3), ylab = "y", xlab = "x")
lines(c(xx), mu, col = "blue")
lines(c(xx), lower, col = "blue", lty = 2)
lines(c(xx), upper, col = "blue", lty = 2)
points(c(X), Y, col = "red")
points(c(best$x), new.Y, col = "green")
par(oldpar)

### RMSE ###
print(sqrt(mean((ftrue - mu))^2))

Prediction of the MuFiMeshGP emulator for any fidelity level.

Description

The function computes the posterior mean and standard deviation of the MuFiMeshGP model.

Usage

MuFiMeshGP(
  X,
  t,
  Y,
  covtype = "Gaussian",
  trend.type = "OK",
  trend.dim = "input",
  trend.pol = "quadratic",
  interaction = NULL,
  mean.known = NULL,
  H.known = NULL,
  gradient = TRUE,
  init = NULL,
  single_fidelity = FALSE,
  param.bounds = NULL,
  iso = FALSE,
  l = 4,
  nugget = 1e-06,
  ncores = 1
)

Arguments

X

matrix of input locations. Each row represents a sample.

t

vector of fidelity levels. Each element is a sample and is connected to the corresponding row in X.

Y

vector of response values.

covtype

covariance kernel type, only 'Gaussian' is available for now, 'Matern5_2' or 'Matern3_2' will be available soon (see cov_gen).

trend.type, trend.dim, trend.pol, interaction

define the mean function form of the Gaussian process. trend.type can be: "SK" in which case mean.known needs to be specified as a scalar; "OK" in which case the constant mean will be evaluated through MLE; "UK" in which case trend.dim specifies whether the trend will be along the input space ("input"), the fidelity space ("fidelity"), or both ("both"). If trend.dim is "input" or "both", the user can use the trend.pol to specify if the trend on the input space alone should be "linear" or "quadratic}. Finally, if \code{trend.dim} is \code{"both"}, then an \code{interaction} term specify the polynomial order (\code{"linear or "quadratic") of the input space trend that is multiplied to the fidelity space trend. See regF_gen for further details.

mean.known

Specifies the mean if "SK" as trend.type, scalar.

H.known

allow the user to specify the value of H as H.known, a scalar in (0,1).

gradient

whether or not the gradient of the log-likelihood shouldbe used in the parameter estimation.

init

Where should the parameter estimation start from, a vector.

single_fidelity

can be used as TRUE to use MuFiMeshGP as a single fidelity Gaussian Process. This will set sigma2sq as 0.

param.bounds

a list with two arguments(lower and upper) describing the bounds used for MLE optimization of phi1sq and phi2sq. Each argument should be a vector of length ncol(X). If NULL the bounds of phi1sq and phi2sq are specified automatically from the design matrix.

iso

whether the covariance function will be isotropic (TRUE or FALSE)

l

rate of convergence of the system (see Details), scalar.

nugget

(optional) for controlling numerical error.

ncores

(optional) number of cores for parallelization.

Details

From the model fitted by MuFiMeshGP or update.MuFiMeshGP the posterior mean and standard deviation are calculated for any input location and fidelity level. For details, see Boutelet and Sung (2025, <arXiv:2503.23158>).

Value

a list which is given the S3 class "MuFiMeshGP"

See Also

MuFiMeshGP for the model.

Examples

# Example code

f <- function(x, t){
  x <- c(x)
  return(exp(-1.4*x)*cos(3.5*pi*x)+sin(40*x)/10*t^2)
}

set.seed(1)
X <- matrix(runif(15,0,1), ncol = 1)
tt <- runif(15,0.5,2)

Y <- f(c(X), tt)

fit.mufimeshgp <- MuFiMeshGP(X, tt, Y)

xx <- matrix(seq(0,1,0.01), ncol = 1)
ftrue <- f(xx, 0)

# predict
pred.mufimeshgp <- predict(fit.mufimeshgp, xx, rep(0,101))

mu <- pred.mufimeshgp$mean
s <- pred.mufimeshgp$sd
lower <- mu + qnorm(0.025)*s
upper <- mu + qnorm(0.975)*s

# plot

oldpar <- par(mfrow = c(1,1))
plot(xx, ftrue, "l", ylim = c(-1,1.3), ylab = "y", xlab = "x")
lines(c(xx), mu, col = "blue")
lines(c(xx), lower, col = "blue", lty = 2)
lines(c(xx), upper, col = "blue", lty = 2)
points(c(X), Y, col = "red")
par(oldpar)

### RMSE ###
print(sqrt(mean((ftrue - mu))^2))

Generates the covariance matrix

Description

Generates the covariance matrix for the Gaussian kernel or Matern kernel

Usage

cov_gen(
  x1,
  x2 = NULL,
  t1,
  t2 = NULL,
  phi1sq,
  phi2sq,
  sigma1sq,
  sigma2sq,
  H,
  l = 4,
  covtype,
  iso,
  nugget = sqrt(.Machine$double.eps)
)

Arguments

x1, x2

Design input location matrices. x2 is to be used only to create cross-covariance matrix.

t1, t2

Design tunable parameter vectors. t2 is to be used only to create cross-covariance matrix.

phi1sq, phi2sq, sigma1sq, sigma2sq, H, l

hyper-parameters for the covariance function

covtype

kernel function used: "Gaussian" is the only available one at the moment.

iso

If TRUE, then the covariance function is isotropic. If FALSE, the covariance function is anisotropic.

nugget

optional

Value

a matrix of size (nrow(x1), nrow(x2)), or (nrow(x1), nrow(x1)) if x2 and t2 are NULL.

predict.MuFiMeshGP

Description

The function computes the posterior mean and standard deviation of the MuFiMeshGP model.

Usage

## S3 method for class 'MuFiMeshGP'
predict(object, x, t, ...)

Arguments

object

an object of class MuFiMeshGP.

x

matrix of new input locations to predict.

t

vector or new fidelity levels to use for predictions.

...

no other argument.

Details

Prediction of the MuFiMeshGP emulator for any fidelity level.

From the object fitted by MuFiMeshGP or update.MuFiMeshGP the posterior mean and standard deviation are calculated for any input location and fidelity level. For details, see Boutelet and Sung (2025, <arXiv:2503.23158>).

Value

See Also

MuFiMeshGP for the model

Examples

# Example code
f <- function(x, t){
  x <- c(x)
  return(exp(-1.4*x)*cos(3.5*pi*x)+sin(40*x)/10*t^2)
}

set.seed(1)
X <- matrix(runif(15,0,1), ncol = 1)
tt <- runif(15,0.5,2)

Y <- f(c(X), tt)

fit.mufimeshgp <- MuFiMeshGP(X, tt, Y)

xx <- matrix(seq(0,1,0.01), ncol = 1)
ftrue <- f(xx, 0)

# predict
pred.mufimeshgp <- predict(fit.mufimeshgp, xx, rep(0,101))

mu <- pred.mufimeshgp$mean
s <- pred.mufimeshgp$sd
lower <- mu + qnorm(0.025)*s
upper <- mu + qnorm(0.975)*s

# plot

oldpar <- par(mfrow = c(1,1))
plot(xx, ftrue, "l", ylim = c(-1,1.3), ylab = "y", xlab = "x")
lines(c(xx), mu, col = "blue")
lines(c(xx), lower, col = "blue", lty = 2)
lines(c(xx), upper, col = "blue", lty = 2)
points(c(X), Y, col = "red")
par(oldpar)

### RMSE ###
print(sqrt(mean((ftrue - mu))^2))

Creates the regression function for the mean

Description

Creates the regression function for the GP mean.

Usage

regF_gen(trend.dim, trend.pol, interaction, l, d)

Arguments

trend.dim

which dimension should the trend follow: "input", "fidelity", or "both".

trend.pol

Which polynomial degree should the input mean trend have: "linear" or "quadratic".

interaction

polynomial degree of the interaction between input trend and fidelity trend of: NULL, "linear", or "quadratic". "linear" or "quadratic".

l

convergence rate parameter, usually l = 4.

d

input space dimension

Value

a function

update.MuFiMeshGP

Description

The function updates the current MuFiMeshGP model.

Usage

## S3 method for class 'MuFiMeshGP'
update(object, x, t, y, param.estim = TRUE, init = NULL, ...)

Arguments

object

an object of class MuFiMeshGP.

x

matrix of new input locations.

t

new tunable parameter, a scalar.

y

observation corresponding to input location x and tunable parameter t.

param.estim

if TRUE, the hyper-parameters are estimated by running it through MuFiMeshGP. If FALSE, the hyper-parameters from object are used to update the MuFiMeshGP model fit.

init

See MuFiMeshGP.

...

no other argument.

Details

Updates the MuFiMeshGP model fit with new observations

From the model fitted by MuFiMeshGP or update.MuFiMeshGP the posterior mean and standard deviation are calculated for any input location and fidelity level. For details, see Boutelet and Sung (2025, <arXiv:2503.23158>).

Value

a list which is given the S3 class "MuFiMeshGP"

See Also

MuFiMeshGP for initializing the model.

Examples

# Example code

f <- function(x, t){
  x <- c(x)
  return(exp(-1.4*x)*cos(3.5*pi*x)+sin(40*x)/10*t^2)
}

set.seed(1)
X <- matrix(runif(15,0,1), ncol = 1)
tt <- runif(15,0.5,2)

Y <- f(c(X), tt)

fit.mufimeshgp <- MuFiMeshGP(X, tt, Y)

xx <- matrix(seq(0,1,0.01), ncol = 1)
ftrue <- f(xx, 0)

# predict
pred.mufimeshgp <- predict(fit.mufimeshgp, xx, rep(0,101))

mu <- pred.mufimeshgp$mean
s <- pred.mufimeshgp$sd
lower <- mu + qnorm(0.025)*s
upper <- mu + qnorm(0.975)*s

# plot

oldpar <- par(mfrow = c(1,2))
plot(xx, ftrue, "l", ylim = c(-1,1.3), ylab = "y", xlab = "x")
lines(c(xx), mu, col = "blue")
lines(c(xx), lower, col = "blue", lty = 2)
lines(c(xx), upper, col = "blue", lty = 2)
points(c(X), Y, col = "red")

### RMSE ###
print(sqrt(mean((ftrue - mu))^2))

best <- IMSPE_AL(fit.mufimeshgp, 0.5, 2, function(t) return(1 / t^2))
new.Y <- f(best$x, best$t)
fit.mufimeshgp <- update(fit.mufimeshgp, best$x, best$t, new.Y)

pred.mufimeshgp <- predict(fit.mufimeshgp, xx, rep(0, 101))
mu <- pred.mufimeshgp$mean
s <- pred.mufimeshgp$sd
lower <- mu + qnorm(0.025)*s
upper <- mu + qnorm(0.975)*s

plot(xx, ftrue, "l", ylim = c(-1,1.3), ylab = "y", xlab = "x")
lines(c(xx), mu, col = "blue")
lines(c(xx), lower, col = "blue", lty = 2)
lines(c(xx), upper, col = "blue", lty = 2)
points(c(X), Y, col = "red")
points(c(best$x), new.Y, col = "green")

par(oldpar)

### RMSE ###
print(sqrt(mean((ftrue - mu))^2))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.