On-Demand Webinars: Aerospace

Machine Learning for Narrowing the Simulation-Test Gap


Establishing how well a numerical simulation represents reality is critical for making simulation results more trustworthy for decision makers. This 60-minute Webinar will focus on statistical calibration, a machine learning process used to quantify uncertainties (both parameter and model form) in simulations as a means for narrowing the simulation – physical test gap. A calibrated machine learning model of a simulation even can be used directly for prediction, allowing for rapid analyses that wouldn’t be possible via direct evaluation of the simulation.

Topics include:

  • An introduction to the underpinning ideas and benefits of statistical calibration using examples
  • Statistical calibration’s unique ability to account for both parameter and model-form uncertainties
  • Frequentist and Bayesian calibration options

An audience Q&A will follow the technical presentation.


Gavin Jones, Principal Application Engineer, SmartUQ

As the Principal Application Engineer at SmartUQ, Gavin Jones is responsible for performing simulation and statistical work for clients in the automotive, aerospace, defense, gas turbine, and other industries. He is a member of the SAE Chassis Committee as well as a member of AIAA’s Digital Engineering Integration Committee. Gavin also is a key contributor to SmartUQ’s Digital Twin/Digital Thread initiative.


Amanda Hosey, Editor, SAE Media Group

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