Fast, Accurate Bayesian Optimization for Engineering Simulation and Experimentation
On Demand
As engineering simulations and experimentation grow more computationally expensive and complex, traditional optimization approaches often become impractical or infeasible. Bayesian Optimization offers a powerful, data-efficient alternative that combines machine learning with statistical decision making to optimize while minimizing the amount of simulation or experimentation required.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce SmartUQ’s tools for Bayesian Optimization. The superior speed and accuracy of SmartUQ’s Bayesian Optimization will be covered through benchmarks and example problems.
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.