A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
Probabilistic Response mOdel Fitting with Interactive Tools
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Generate orthogonal polynomials for arbitrary probability density functions
This repository includes Matlab codes/routines that were used in my Bachelor thesis entitled "Numerical Methods For Uncertainty Quantification In Option Pricing" that can be found in: https://www.researchgate.net/publication/330005261_Numerical_Methods_For_Uncertainty_Quantification_In_Option_Pricing.
Arbitrary Polynomial Chaos Toolkit
A source code for the paper titled "Global Sensitivity Analysis using Polynomial Chaos Expansion on the Grassmann Manifold".
This suite is an ensemble of codes developed to conduct a global sensitivity analysis on an multimodal energy harvesting system with periodic excitation.
Codes used for the results in the paper: Sensitivity Analysis for a long-time clogging simulation code.
Intention-aware control using stochastic expansion methods
Presentations for Geilo Winter School 2015
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