Generate orthogonal polynomials for arbitrary probability density functions
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Updated
Dec 9, 2017 - Python
Generate orthogonal polynomials for arbitrary probability density functions
Presentations for Geilo Winter School 2015
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.
This suite is an ensemble of codes developed to conduct a global sensitivity analysis on an multimodal energy harvesting system with periodic excitation.
Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
A source code for the paper titled "Global Sensitivity Analysis using Polynomial Chaos Expansion on the Grassmann Manifold".
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Codes used for the results in the paper: Sensitivity Analysis for a long-time clogging simulation code.
Arbitrary Polynomial Chaos Toolkit
Probabilistic Response mOdel Fitting with Interactive Tools
A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
Intention-Aware Control Using Stochastic Expansion Methods
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