An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
Project description
BayesValidRox
An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models. This framework provides an automated workflow for surrogate-based sensitivity analysis, Bayesian calibration, and validation of computational models with a modular structure.
Authors
Installation
Install my project with pip
pip install bayesvalidrox
Features
- Surrogate modeling with Polynomial Chaos Expansion
- Global sensitivity analysis using Sobol Indices
- Bayesian calibration with MCMC using
emceepackage - Bayesian validation with model weights for multi-model setting
Requirements
- numpy==1.22.1
- pandas==1.2.4
- joblib==1.0.1
- matplotlib==3.4.2
- seaborn==0.11.1
- scikit-learn==0.24.2
- tqdm==4.61.1
- chaospy==4.3.3
- emcee==3.0.2
- tables==3.6.1
- corner==2.2.1
- h5py==3.2.1
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