mogp_emulator is a Python package for fitting Gaussian Process Emulators to computer simulation results.
The code contains routines for fitting GP emulators to simulation results with a single or multiple target
values, optimizing hyperparameter values, and making predictions on unseen data. The library also implements
experimental design, dimension reduction, and calibration tools to enable modellers to understand complex
computer simulations.
The following pages give a brief overview of the package, instructions for installation, and an end-to-end
tutorial describing a Uncertainty Quantification workflow using mogp_emulator. Further pages outline
some additional examples, more background details on the methods in the MUCM Toolkit, full implementation
details, and some included benchmarks.
.. toctree:: :maxdepth: 1 :caption: Introduction and Installation: intro/overview intro/installation intro/tutorial intro/methoddetails
.. toctree::
:maxdepth: 1
:caption: Some more specific demos and tutorial illustrating how the various package components can
be used are:
demos/gp_demos
demos/multioutput_tutorial
demos/gp_kernel_demos
demos/mice_demos
demos/historymatch_demos
demos/kdr_demos
demos/gp_demoGPU
demos/gp_demoR
demos/excalibur_workshop_demo
.. toctree::
:maxdepth: 1
:caption: For a more detailed description of some of the Uncertainty Quantification methods used in
this package, see the MUCM toolkit pages:
methods/methods
.. toctree:: :maxdepth: 1 :caption: Detailed information on all implemented classes and functions are described in the following pages: implementation/implementation
.. toctree:: :maxdepth: 1 :caption: For some more specifics on benchmarks involving the implementation, see the following benchmark examples: benchmarks/benchmarks