Practitioners who construct complex simulation models of critical systems know that replicating real-world performance is challenging due to uncertainties in simulation, physical tests, and sensor data. These uncertainties arise from sources such as measurement inaccuracies, material property variation, boundary and initial conditions, and modeling approximations. This webinar will introduce Machine Learning (ML) and Uncertainty Quantification (UQ) methods, benefits, and tools with a focus on their use in solving automotive engineering challenges.
UQ is a ML process that puts error bands on results by incorporating real world variability and probabilistic behavior into engineering and systems analysis. UQ answers the question: what is likely to happen when the system is subjected to uncertain and variable inputs. Answering this question facilitates significant risk reduction, robust design, and greater confidence in engineering decisions. Modern UQ techniques use powerful statistical models to map the input-output relationships of the system, significantly reducing the number of simulations or tests required to get statistically defensible answers.
This webinar will discuss basic ML and UQ methods such as Gaussian processes, sensitivity analysis, uncertainty propagation, and statistical calibration.
This event qualifies for 1 PDH
About the speaker: Gavin Jones, Principal Application Engineer at SmartUQ, 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 is also a key contributor in SmartUQ’s Digital Twin/Digital Thread initiative.