# Bonding-time, or should I say coding time?

## Starting out

Hello everyone!

I started off the first half of bonding time just reading about what I would need to implement, the libraries I would need to use and best practices for Julia code.

However, I slowly started getting bored just reading and reading. I figured that starting coding earlier would be my best bet, so that's exactly what I did!

I think there are a lot of good reasons for that: I will hit road blocks earlier, thus solving them earlier. This means no all nighters (hopefully) before deadlines. Moreover, if everything goes according to plan I might manage to code the bonus part of my proposal, win-win!

Let's see what I accomplished so far.

## One dimensional response surfaces

Response surfaces account for 1/3 of the functionality of my new library: the main idea is to mix polynomial interpolation with radial basis function interpolation. In these two weeks I just tackled the

The interpolation in my case is mainly done with basis functions, plus a two/three degree polynomial to it to make the system more stable.

The thing I love the most about my project is that the math is scary looking, however once understood the code being written turns out to be quite simple.

Suppose we choose a polynomial base** Chebyshev** polynomials is the safest one. In the next article you will discover the choice for the general case ;-)!

If I have a set of n samples

To find the

The first

Now that these two conditions are clear, we can just set up a linear system

A neat property to observe: A is** always **symmetric. How beautiful!

## Kriging: using a very special basis function

Suppose we have the following basis function:

where:

is the dimension of models how active is the function in the coordinate determines the smoothness of the function in the coordinate. High smoother function

We can then build the** Krigin predictor.**

The idea is the same as before, however with Kriging we have a statistical interpretation of the error at a point. That is, besides giving an estimate at a new point, the method will also explicitly say how sure it is about said prediction.

We start by defining the** correlation** between two sampled data point as:

That is, two values are highly correlated if they are closed together in space. This definition as the neat property that as the distance goes to infinity the correlation tends to

Now, sparing the mathematical detail, we can construct the estimator by maximing the log of the likelihood function. We then get, for suitable

For every point we can also calculate the mean squared error at that point. Of course, if we try to calculate the error at point from the sample

You can find the code for this two methods here. (Bear in mind, no review has been done at the time of this article, things are still rough).

## What to do in the next two weeks?

My plan for the next two weeks is to polish the code for the one dimensional case, then build the general N-dimensional case. The code should be very similar so it should not be an issue. After that I will write some tests to make sure everything is working as intended. I will also write some documentation and a few example drawn from nasty differential equations to show the functionality of the surrogates in different cases. I hope to do this in two weeks even though it may be a bit of a stretch.

We will see how it goes!

Happy coding,

Ludovico