delismm 1.6.7

kriging-based metamodels: doe, sensitivity, kriging creation, model improvement, optimization, assessment

delismm

Delismm is a pure python package for the creation of kriging-based metamodels.

It covers the following features:

• Perform parameter sensitivity analysis
• Generate Design of Experiments (DOE)
• Save/Load DOE
• Create Kriging Metamodels
• Create hierarchical kriging metamodels
• Perform a resampling to generate new Designs and improve the model
• Run DOEs using various parallelization methods local and remote

Installation and Usage

At least you require Python >= 3.9 to run delismm. To install it, extract the archive and perform the following steps:

cd delismm
python setup.py install

A simple example:

from delismm.example import runExample
runExample()

This will create a

• doe
• sample values
• kriging model
• a diagram

Contributing to delismm

We welcome your contribution!

If you want to provide a code change, please:

• Create a fork of the GitLab project.
• Develop the feature/patch
• Provide a merge request.

If it is the first time that you contribute, please add yourself to the list of contributors below.

Citing

If you use this work in a scientific publication, please cite the specific version that you used as follows:

Sebastian Freund : "delismm", <RELEASE_NUMBER>, <Publication_Date>, <Git_Repository_URL>

You can find information about the release number and the publication date in the changelog.

License

MIT

Change Log

see changelog

Authors

Sebastian Freund