Bayesian Optimization with Gaussian Process as surrogate model
Project description
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
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