Uncertainty Quantification Python Laboratory
(UQPyL)

UQPyL: The Uncertainty Quantification Python Laboratory provide comprehensive workflows tailored to the Uncertainty Quantification and Optimization for computational models and their associated applications (e.g. model calibration, resource scheduling, product design).

The main characteristics of UQPyL includes:

1. Implementation of widely used sensitivity analysis methodologies and optimization algorithms.

2. Integration of diverse surrogate models equipped with tunable to solving computational expensive problems.

3. Provision of a comprehensive suite of benchmark problems and practical case studies, enabling users to quick start.

4. A modular and extensible architecture that encourages and facilitates the development of novel methods or algorithms by users, aligning with our commitment to openness and collaboration. (We appreciate and welcome contributions)

Website: http://www.uq-pyl.com/ (#TODO it need to update now.)
Source Code: https://github.com/smasky/UQPyL/
Documentation: #TODO
Citing in your work: #TODO

Included Methods and Algorithms

Sensibility Analysis: (all methods support for surrogate models)

• Sobol'
• Delta_test (DT)
• extended Fourier Amplitude Sensitivity Test (eFAST)
• Random Balance Designs - Fourier Amplitude Sensitivity Test
• Multivariate Adaptive Regression Splines-Sensibility Analysis (MARS-SA)
• Morris
• Regional Sensitivity Analysis (RSA)

Optimization Algorithms: (* indicates the use of surrogate models)

• SCE-UA
• Genetic Algorithm (GA)
• Non-dominated Sorting Genetic Algorithm-II (NSGA-II)
• AMSMO*
• MO_ASMO*
• MASTO* #TODO
• AMSMO* #TODO

Surrogate Models:

• Full connect neural network (FNN)
• Kriging (KRG)
• Gaussian Process (GP)
• Linear Regression (LR)
• Polynomial Regression (PR)
• Radial Basis Function (RBF)
• Support Vector Machine (SVM)
• Multivariate Adaptive Regression Splines (MARS)

Installation

Recommend (PyPi or Conda):

pip install UQPyL

conda install UQPyL

And also:

git clone https://github.com/smasky/UQPyL.git 
pip install . 

Call for Contributions

We appreciate and welcome contributions. Because, we only set up standard workflows here. More advanced quantification methods and optimization algorithms are waited for pulling to this project.

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Contact:

wmtSky, wmtsky@hhu.edu.cn