SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden

• Sampling:

  • Various stochastic and spectral types of samples

• Uncertainty propagation or UQ forward problem:

  • generalized Polynomial Chaos Expansion (gPCE)
  • Probabilistic PCE (PPCE)

• Global sensitivity analysis (GSA):

  • Sobol sensitivity indices

• Surrogates:

  • Lagrange interpolation
  • gPCE
  • Gaussian process regression (GPR)

pip install UQit

The html documentation is on this GitHub page

The tests associated to different UQ techniques are provided in ./tests/. Most of the tests have also been used in the notebooks and documentation. For instance, to run a test from pce_tests.py in ./tests/, do the following:

python3 -c 'import pce_tests as X;X.pce_1d_test()'

• General
  • numpy
  • scipy
  • matplotlib
• Optional
  • cvxpy (for compressed sensing in PCE)
  • PyTorch (for GPR)
  • GPyTorch (for GPR)

• In case there is a bug, please feel free to open an issue on Github.

• Qestions/comments:

  • Saleh Rezaeiravesh, salehr@kth.se
    
  • Ricardo Vinuesa, rvinuesa@mech.kth.se
  • Philipp Schlatter, pschlatt@mech.kth.se
    

• Rezaeiravesh S., Vinuesa R., Schlatter P., UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD). Journal of Open Source Software, 6(60), 2871, 2021.

• Rezaeiravesh S., Vinuesa R., Schlatter P., On numerical uncertainties in scale-resolving simulations of canonical wall turbulence, Computers & Fluids, 227:105024, 2021.

• Rezaeiravesh S., Vinuesa R., Schlatter P., An Uncertainty-Quantification Framework for Assessing Accuracy, Sensitivity, and Robustness in Computational Fluid Dynamics, arXiv:2007.07071, 2020.

Source code, documentation, tests and notebooks are provided for the above-listed features.