Metadata-Version: 2.1
Name: pyoptmat
Version: 1.1.2
Summary: Statistical inference for material models
Home-page: https://github.com/Argonne-National-Laboratory/pyoptmat
Author: Argonne National Laboratory
Author-email: messner@anl.gov
License: UNKNOWN
Description: # pyoptmat: statistical inference for material models 
        
        ![Test Status](https://github.com/Argonne-National-Laboratory/neml/workflows/tests/badge.svg?branch=master) [![Documentation Status](https://readthedocs.org/projects/neml/badge/?version=stable)](https://neml.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).
        
Keywords: materials inference modeling
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
