Design of Experiments, Calibration, and Machine Learning for RecurDyn Simulations
On Demand
Getting the most out of RecurDyn simulations requires efficient use of the available simulation budget as well as validating that the simulation produces results that agree with reality. This webinar will discuss the role design of experiments, predictive machine learning models (aka surrogate models), and machine learning tools such as statistical calibration can play in calibrating RecurDyn simulations 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 RecurDyn simulation applications. SmartUQ’s ability to integrate with RecurDyn will also be demonstrated and discussed.
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.