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ME597/AAE590: Introduction to Uncertainty Quantification

By Alina Alexeenko

Aeronautics and Astronautics, Purdue University, West Lafayette, IN



Published on


A full course, "Introduction to Uncertainty Quantification" is offered on A subset of lectures related to nanotechnology is available here.

The focus of the course is on the quantification of uncertainty in multiscale multiphsyics simulations for engineering analysis. Though engineering simulation has become the mainstay of academic and industrial analysis in recent years, there has been little emphasis in understanding and quantifying the source of uncertainty in predictions. These uncertainties may arise for a variety of reasons: lack of mesh-independence, inadequate physical models, uncertainties in geometry, operating conditions and material properties, among others. The course introduces the student to the concepts of verification and validation, sensitivity analysis, and uncertainty propagation using sampling methods, polynomial chaos and reliability based methods. An introduction to uncertainty quantification software is also given. Emerging topics in uncertainty quantification of microsystem simulations are presented.


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Researchers should cite this work as follows:

  • Alina Alexeenko (2011), "ME597/AAE590: Introduction to Uncertainty Quantification,"

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Birck Nanotechnology Center, Purdue University, West Lafayette, IN


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Lecture Number/Topic Online Lecture Video Lecture Notes Supplemental Material Suggested Exercises
Uncertainty Quantification in Experiments View Flash View Notes
An overview is provided of how experimental data should be reported with true uncertainties. Examples from experiments on gas damping measurements in RF switches and for estimation of...

Uncertainty Propagation in a Multiscale Model of Nanocrystalline Plasticity View Flash View Notes
Uncertainty Quantification of Molecular Dynamics Simulations View Flash View Notes, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.