Gaussian process regression example for pyiron

This repository contains a Jupyter notebook workflow.ipynb which demonstrates a workflow to run a Gaussian process regression on some simple training data. The workflow is implemented using pyiron. The corresponding pyiron class can be found in gaussian_progress_regression.py. This workflow has been developed within the BMBF-funded project DiMoGraph (Digital models for graphene-based conductor materials, 13XP5190B).

Contributers:

Florian Fuchs (Fraunhofer ENAS, Chemnitz)

Philipp Schulze (TU Berlin, Berlin)

Fabian Teichert (Fraunhofer ENAS, Chemnitz)

Requirements:

pyiron

pyiron_base

scikit-learn

numpy

optuna