grseb9s / rbd-fast

RBD Fast sensitivity Analysis

RBD-FAST on Python

This is an implementation of sensitivity analysis algorithm RBD-FAST

This implementaton is from Sarah JURICIC, based on Matlab code from Mickael RABOUILLE. RBD-FAST is known to be a robust sensitivity analysis method for model with large computationnal cost.

Original algorithm:

Author: S. Tarantola (JRC) Joint Research Centre All rights Reserved

Update: M. Rabouille

Add: Reordering Y according a random design X (EASI algorithm) from E Plischke. Add: Unbiased estimator from J-Y Tissot & C Prieur. Note: The estimate is less dependant on the M value which can be raised up to 10.

References:

S. Tarantola, D. Gatelli and T. Mara (2006) Random Balance Designs for the Estimation of First Order Global Sensitivity Indices, Reliability Engineering and System Safety, 91:6, 717-727

Elmar Plischke (2010) An effective algorithm for computing global sensitivity indices (EASI) Reliability Engineering & System Safety, 95:4, 354-360. <10.1016/j.ress.2009.11.005>

Jean-Yves Tissot, Clémentine Prieur (2012) Bias correction for the estimation of sensitivity indices based on random balance designs. Reliability Engineering and System Safety, Elsevier, 107, 205-213. <10.1016/j.ress.2012.06.010>

Jeanne Goffart, Mickael Rabouille & Nathan Mendes (2015): Uncertainty and sensitivity analysis applied to hygrothermal simulation of a brick building in a hot and humid climate, Journal of Building Performance Simulation, DOI: 10.1080/19401493.2015.1112430

Contributors

Any contribution is welcomed. If you find any bug, don't hesitate to open an issue or submit a PR with fixes.

License

To be defined soon