https://github.com/argonne-national-laboratory/pyoptmat

pytorch/pyro-based package for calibrating statistical constitutive models to experimental data
https://github.com/argonne-national-laboratory/pyoptmat

Last synced: about 1 month ago
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pytorch/pyro-based package for calibrating statistical constitutive models to experimental data

• Host: GitHub
• URL: https://github.com/argonne-national-laboratory/pyoptmat
• Owner: Argonne-National-Laboratory
• License: other
• Created: 2021-07-13T01:21:50.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-03-22T02:07:51.000Z (about 1 year ago)
• Last Synced: 2024-04-22T23:45:28.017Z (about 1 year ago)
• Language: Python
• Size: 13.6 MB
• Stars: 5
• Watchers: 5
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pyoptmat: statistical inference for material models 

[![Run test suite](https://github.com/Argonne-National-Laboratory/pyoptmat/actions/workflows/tests.yml/badge.svg?branch=master)](https://github.com/Argonne-National-Laboratory/pyoptmat/actions/workflows/tests.yml) [![Documentation Status](https://readthedocs.org/projects/pyoptmat/badge/?version=stable)](https://pyoptmat.readthedocs.io/en/stable/)

pyoptmat is a package for calibrating statistical material

models to data.  The package is based on [pytorch](https://pytorch.org/)

and [pyro](https://pyro.ai/) and provides a framework for using machine-learning

techniques to calibrate deterministic and statistical models against

experimental data.

A “material model” is mathematically a parameterized system of ordinary 

differential equations which, integrated through the experimental conditions, 

returns some simulated output that can be compared to the test measurements.

pyoptmat uses Bayesian inference with the pyro package to find statistical

distributions of the model  parameters to explain the variation in the 

experimental data.

As an example, consider a collection of tension test data on several samples 

of a material. The test measurements have some variation caused by 

manufacturing variability and uncertainty in the experimental controls and 

measurements.

![Example of fitting a statistical model to data](doc/sphinx/figures/demonstration.png)

pyoptmat aims to make training a statistical model to capture these 

variations easy. The image shows the results of training a simple material 

model to the test data. The trained statistical model captures the 

variability in the experimental data and can then be used to translate 

this uncertainty to models of engineering components. Transferring 

uncertainty quantified in experimental measurements to predictions of 

uncertainty in engineering applications is the main reason pyoptmat was 

developed.

The software is provided under an [MIT license](LICENSE).  Full

documentation is available [here](https://pyoptmat.readthedocs.io).