Gaussian process regression using pyiron

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DiMoGraph Gaussian Process Regression

Version: v0.1.0

Author(s): Florian Fuchs <florian.fuchs@enas.fraunhofer.de>, Philipp Schulze <pschulze@math.tu-berlin.de>, Fabian Teichert <fabian.teichert@enas.fraunhofer.de>

pyiron skikit-learn Gaussian process regression
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Demonstrate performing Gaussian Process Regresssion of some test data with pyiron.

Source:
https://github.com/shcufnairolf/gaussian_process_regression_using_pyiron

README.md

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