GPGO 0.1

Bayesian Optimization with Gaussian Process as surrogate model

GPGO - Gaussian Process GO

My own implementation of Bayesian Black box Optimization with Gaussian Process as a surrogate model. It is still in development as I'm using it for my Master degree thesis to achieve a bottom up optimization of the Dissipative Particle Dynamics force field for a complex system of polymers chains functionalized gold nanoparticles in a water solvent.

Maximizing the Acquisition function (EI only for now)

In this little package right now there are 3 ways to run an optimization task with Gaussian Processes:

-NAIVE : AkA sampling the acquisition function with a grid of some kind or a quasi random methods as LHS

-BFGS : Find the Maxima of the Acquisition function by using the L-BFGS-B optimizer

-DIRECT : Find the Maxiam of the Acquisition function by using the DIRECT optimizer (need the DIRECT python package)

TODO

-An integration with LAMMPS using the pyLammps routine

-Tutorials and Examples