Learn about
Learn about
Uncertainty Quantification
Uncertainty Quantification
Embedded Files
This page covers Uncertainty Quantification (UQ) broadly:
from forward to inverse UQ, probabilistic and non-probabilistic approaches, Probabilistic Machine Learning (although we often refer to it with the less fancier names: Bayesian inference), fundamentals of sampling strategies, and also our recent works on UQ.
Codes are shared whenever possible.
This page covers Uncertainty Quantification (UQ) broadly:
from forward to inverse UQ, probabilistic and non-probabilistic approaches, Probabilistic Machine Learning (although we often refer to it with the less fancier names: Bayesian inference), fundamentals of sampling strategies, and also our recent works on UQ.
Codes are shared whenever possible.
Note: the page is under development - last updated: 08/2022
Useful online resources
Useful online resources
Many excellent videos on a broad range of UQ topics by Pete Green - link to his YouTube channel and his GitHub page
to be updated - feel free to suggest others!
DVU group resources
DVU group resources
A very brief intro to Uncertainty Quantification
Otherwise known as: trying to fit in everything you think is important in 42 slides! Please note that it is a very biased view.
A very brief intro to Uncertainty Quantification
Otherwise known as: trying to fit in everything you think is important in 42 slides! Please note that it is a very biased view.
Cicirello_Intro_to_UQ_082022.pdfSlides on fundamental UQ topics
Slides on fundamental UQ topics
- (A very brief) intro to Sampling and Monte Carlo Simulations
Intro_to_sampling.pdf- Sampling methods: Inverse Transform Sampling
Sampling_methods_07_2022.pdf
- Sampling methods: Box-Mueller algorithm
Sampling method_box_mueller_07_2022.pdf
- Sampling methods: Accept/reject algorithm
accept_reject_sampling.pdf
Tutorials (with codes)
Tutorials (with codes)
Learning Sampling Methods for solving Bayesian Model Updating Problems with a guided tutorial with Matlab examples!
Paper: Lye A., Cicirello A., Patelli E., Mechanical System and Signal Processing, 2021, https://doi.org/10.1016/j.ymssp.2021.107760
Full text (preprint) freely available here
Matlab codes available here
Research seminars on UQ topics
Research seminars on UQ topics
11/03/2022 - DDPS online seminar | Interpretable, Explainable and Non-Intrusive Uncertainty Propagation by Alice Cicirello
This talk mainly covers how to propagate efficiently interval uncertainty by exploiting a statistical machine learning tool: Bayesian Optimization.
The paper associated with this talk is:
Cicirello A., Giunta F., Machine Learning based optimization for interval uncertainty propagation. Mechanical System and Signal Processing, 2022
Open access: https://doi.org/10.1016/j.ymssp.2021.108619
Repositories with codes developed to learn about Uncertainty Quantification
Repositories with codes developed to learn about Uncertainty Quantification
DVU recent research publications on Uncertainty Quantification
DVU recent research publications on Uncertainty Quantification
Igea F., Cicirello A.,Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation, Mechanical System and Signal Processing, 2022
https://doi.org/10.1016/j.ymssp.2022.109868Igea F., Chatzis M.N.., Cicirello A., On the Combination of Random Matrix Theory with Measurements On a Single Structure. ASME J. Risk Uncertainty Part B, 2022
https://doi.org/10.1115/1.4054172Cicirello A., Giunta F., Machine Learning based optimization for interval uncertainty propagation. Mechanical System and Signal Processing, 2022
Open access: https://doi.org/10.1016/j.ymssp.2021.108619Lye A., Cicirello A., Patelli E., An efficient and robust sampler for Bayesian inference: Transitional Ensemble Markov Chain Monte Carlo, Mechanical System and Signal Processing, 2022
https://doi.org/10.1016/j.ymssp.2021.108471
Matlab codes available hereLye A., Cicirello A., Patelli E., Sampling Methods for solving Bayesian Model Updating Problems: A Tutorial, Mechanical System and Signal Processing, 2021.
https://doi.org/10.1016/j.ymssp.2021.107760
Matlab codes available here
PAGE UNDER DEVELOPMENT
Page updated
Google Sites
Report abuse