Surrogates 101

Let's start with something easy to get our hands dirty. I want to build a surrogate for $f(x) = \log(x) \cdot x^2+x^3$. Let's choose the radial basis surrogate.

using Surrogates
f = x -> log(x)*x^2+x^3
lb = 1.0
ub = 10.0
x = sample(50,lb,ub,SobolSample())
y = f.(x)

#I want an approximation at 5.4
approx = my_radial_basis(5.4)
206.8309775042323

Let's now see an example in 2D.

using Surrogates
using LinearAlgebra
f = x -> x[1]*x[2]
lb = [1.0,2.0]
ub = [10.0,8.5]
x = sample(50,lb,ub,SobolSample())
y = f.(x)

#I want an approximation at (1.0,1.4)
approx = my_radial_basis((1.0,1.4))
7.393838848775778

Kriging standard error

Let's now use the Kriging surrogate, which is a single-output Gaussian process. This surrogate has a nice feature: not only does it approximate the solution at a point, it also calculates the standard error at such point. Let's see an example:

using Surrogates
f = x -> exp(x)*x^2+x^3
lb = 0.0
ub = 10.0
x = sample(100,lb,ub,UniformSample())
y = f.(x)
p = 1.9
my_krig = Kriging(x,y,lb,ub,p=p)

#I want an approximation at 5.4
approx = my_krig(5.4)

#I want to find the standard error at 5.4
std_err = std_error_at_point(my_krig,5.4)
3185.0145640270152

Let's now optimize the Kriging surrogate using Lower confidence bound method, this is just a one-liner:

surrogate_optimize(f,LCBS(),lb,ub,my_krig,UniformSample())

Surrogate optimization methods have two purposes: they both sample the space in unknown regions and look for the minima at the same time.

Lobachevsky integral

The Lobachevsky surrogate has the nice feature of having a closed formula for its integral, which is something that other surrogates are missing. Let's compare it with QuadGK:

using Surrogates
obj = x -> 3*x + log(x)
a = 1.0
b = 4.0
x = sample(2000,a,b,SobolSample())
y = obj.(x)
alpha = 2.0
n = 6
my_loba = LobachevskySurrogate(x,y,a,b,alpha=alpha,n=n)

#1D integral
int_1D = lobachevsky_integral(my_loba,a,b)
int_val_true = int[1]-int[2]
@assert int_1D ≈ int_val_true

Example of NeuralSurrogate

Basic example of fitting a neural network on a simple function of two variables.

using Surrogates
using Flux
using Statistics

f = x -> x[1]^2 + x[2]^2
bounds = Float32[-1.0, -1.0], Float32[1.0, 1.0]
# Flux models are in single precision by default.
# Thus, single precision will also be used here for our training samples.

x_train = sample(100, bounds..., SobolSample())
y_train = f.(x_train)

# Perceptron with one hidden layer of 20 neurons.
model = Chain(Dense(2, 20, relu), Dense(20, 1))
loss(x, y) = Flux.mse(model(x), y)

# Training of the neural network
learning_rate = 0.1
optimizer = Descent(learning_rate)  # Simple gradient descent. See Flux documentation for other options.
n_epochs = 50
sgt = NeuralSurrogate(x_train, y_train, bounds..., model=model, loss=loss, opt=optimizer, n_echos=n_epochs)

# Testing the new model
x_test = sample(30, bounds..., SobolSample())
test_error = mean(abs2, sgt(x)[1] - f(x) for x in x_test)