This project is about exploring Gaussian Process for Regression. We want to create a map of the variation of a property over a 2D space given a few measurements at some specific locations.
I'll start with a synthetic toy model generating a few noisy data from a model with known parameters. Then GP is used to infer the underlying model over the entire space. The idea of measurements are generated along a certain trajectory is also deployed here in this toy model.