Intention-aware control using stochastic expansion methods
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Updated
Sep 7, 2024 - Python
Intention-aware control using stochastic expansion methods
A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
Codes used for the results in the paper: Sensitivity Analysis for a long-time clogging simulation code.
Probabilistic Response mOdel Fitting with Interactive Tools
Arbitrary Polynomial Chaos Toolkit
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
A source code for the paper titled "Global Sensitivity Analysis using Polynomial Chaos Expansion on the Grassmann Manifold".
Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
This suite is an ensemble of codes developed to conduct a global sensitivity analysis on an multimodal energy harvesting system with periodic excitation.
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.
Presentations for Geilo Winter School 2015
Generate orthogonal polynomials for arbitrary probability density functions
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