Supporting Smith: Chapter 10 introduction, Section 10.1 introduction
Corrections/Notes/FAQs:
- None
Stochastic polynomial (also known as spectral) methods are the workhorse of numerical methods for Uncertainty Propagation. By approximating transformations of random variables with polynomials, we can propagate uncertainty through simulation codes accurately, and with significantly fewer simulation solves than Monte Carlo. In this lecture, various techniques are explored to construct these polynomials and they will be investigated numerically in Tutorial 1.
In this part we consider only transformations of one random-variable, i.e. the scalar (1d) case. The next part will discuss generalization to functions of multiple random-variables.
Supporting Smith: Chapter 10 introduction, Section 10.1 introduction
Corrections/Notes/FAQs:
Supporting Smith: Section 10.1.2
Corrections/Notes/FAQs:
Supporting Smith: Section 10.2, especially Sections 10.2.1-2
Corrections/Notes/FAQs:
Supporting Smith: Chapters 10/11
Corrections/Notes/FAQs:
Total time: 1:40:00
dr. R. Dwight ≤r.p.dwight@tudelft.nl≥ - 2022-04-05
