Webinars

Machine Learning and Model Calibration for Simulations and Digital Twins

Digital Engineering
On Demand

As part of a growing shift toward digital engineering, the use of simulation and digital twins is gaining momentum in aerospace, defense, automotive, industrial machinery, and semiconductor industries.

The latest trend is to use machine learning methods to develop surrogate models to bypass the need to run full-physics simulation, saving time and computing costs.

Join our webinar with a LIVE demo. SmartUQ’s principal application engineer, Gavin Jones, will show you how to:

Run space filling Design of Experiments (DOEs)
Set up machine learning models
Conduct sensitivity analysis
Understand uncertainty propagation
Do statistical calibration (Frequentist and Bayesian)
Optimize your models to account for the uncertainty

Presented by Gavin Jones

Principal Application Engineer

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