Uncertainty Quantification
-
Intro
Information
| Primary software used | Jupyter Notebook |
| Course | Uncertainty Quantification |
| Primary subject | AI & ML |
| Secondary subject | Machine Learning |
| Level | Beginner |
| Last updated | N/A |
| Keywords |
Responsible
| Teachers | |
| Faculty |
Uncertainty Quantification 0/0
Uncertainty Quantification
Uncertainty quantification allows us to consider such aspects with mathematical models to integrate into various artificial intelligence methods. So starting from the basics, we are going to introduce different applications in uncertainty.
So starting from the basics, we are going to introduce different applications in uncertainty. We will use structures as examples. Structures are associated with different types of uncertainties including uncertainties in design parameters, construction procedures, response, modelling, and the level of understanding of the physics for the structure. Uncertainty quantification allows us to consider such aspects with mathematical models to integrate into various artificial intelligence methods.
For this tutorial you need to have installed Python, Jupyter notebooks, and some common libraries including Scikit Learn. Please see the following tutorial for more information.
Exercise file
You can download the Python script of this tutorial below.
Write your feedback.
Write your feedback on "Uncertainty Quantification"".
If you're providing a specific feedback to a part of the chapter, mention which part (text, image, or video) that you have specific feedback for."Thank your for your feedback.
Your feedback has been submitted successfully and is now awaiting review. We appreciate your input and will ensure it aligns with our guidelines before it’s published.