Verification, Validation and Uncertainty Quantification

Performing computational modeling and simulation can decrease the number of physical tests necessary for product development. Assuring that the computational model has been formed using sound procedures is key and is achieved through the processes of verification, validation and uncertainty quantification (VVUQ).

Verification is performed to determine if the computational model fits the mathematical description. Validation is implemented to determine if the model accurately represents the real world application. Uncertainty quantification is conducted to determine how variations in the numerical and physical parameters affect simulation outcomes.

Uncertainty quantification, which stands at the confluence of probability, statistics, computational mathematics, and disciplinary sciences, provides a promising framework to answer that question and has gathered tremendous momentum in recent years. In these courses, you will discuss the following aspects of uncertainty quantification:

• the motivation: where are the uncertainties coming from and why they are important?
• the solution
  
• the challenge:
  
  • How hard to implement them in practice?
  • How machine learning can help?