Simulation is integral to numerous industries including the automotive industry; however, the effective use of simulation still presents challenges, one of which is long run times. Long simulation run times limit the analyses and number of inputs, scenarios, and design possibilities that can be considered. The solution is to take a surrogate modeling approach where first a machine learning (ML) model is trained to predict the simulation’s results. The ML model’s rapid prediction of simulation results allows more analyses to be performed and in less time.
SmartUQ is a fast, accurate, and comprehensive ML and Uncertainty Quantification software tool optimally designed for simulation, digital twin, and other engineering applications. SmartUQ includes best in class ML models, which significantly outperform the competition in terms of training speed and predictive accuracy.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will discuss SmartUQ’s tools for accelerating automotive simulations including design of experiments (DOEs), ML models, statistical calibration, and optimization under uncertainty. Points will be illustrated with demonstration in SmartUQ and customer use cases.
