Modern Design of Experiments, Machine Learning, and Calibration for Simulation and Digital Twins
On Demand
Getting the most out of simulation and digital twins requires efficient data collection as well as validating that the simulation or digital twin produces results that agree with reality. This webinar will discuss the role design of experiments, predictive machine learning models, and machine learning tools such as statistical calibration can play in calibrating simulations and digital twins to physical data, validating their accuracy, and maximizing the knowledge gained from their use.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for simulation and digital twin applications. A demonstration of SmartUQ as well as discussion of customer use cases will be included as part of the presentation.
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.