UQPyL 2.0.1

A python package for parameter uncertainty quantification and optimization

Uncertainty Quantification Python Laboratory
(UQPyL)

UQPyL: The Uncertainty Quantification Python Laboratory provide a comprehensive workflow for parameter uncertainty quantification and optimization in computational numerical simulations. UQPyL offers an extensive suite of advanced methodologies(Sobol', Delta Test, EFAST, et al.) and algorithms (NSGA-II, ASMO, MO-ASMO, et al.). In summary, UQPyL consists of four core modules:

• Design of Experiments (DoE)
• Sensibility Analysis
• Optimization
• Surrogate models

The surrogate models Module can help to solve computational expensive problems caused by intensive numerical simulations.**

Once you have clearly defined the problem you aim to address, you can employ all pre-prepared methods and algorithms to complete following task:

• Uncertainty Quantification (UQ)
• Parameter Optimization

Moreover, the versatility of UQPyL allows researchers to craft their own methods or algorithms by incorporating its diverse range of surrogate models. Consequently, users can:

• Evaluate the effectiveness of their custom-designed algorithms

• Compare different methods and algorithms under specific problem scenarios

  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
  

Installation

pip install UQPyL (Recommend)

or

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

cd UQPyL and 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, [e-mail]