Webinars

Getting the most out of ANSYS 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 play in calibrating ANSYS simulations to physical data, validating their accuracy, and maximizing the knowledge gained from their use. All of the above also come together to enable digital twins based on ANSYS simulations. Digital twins must be fast and accurate representations of physical reality. While a properly calibrated ANSYS simulation can deliver the required accuracy, it lacks the predictive speed required for the digital twin model to run in real time with its physical counterpart. In SmartUQ surrogate models with best in class predictive accuracy can easily be trained of ANSYS simulations and calibrated to physical data, leading to fast and accurate digital twins. Join us for this webinar in which SmartUQ Principal Application Engineer, Gavin Jones, will introduce the use of SmartUQ for ANSYS simulation and digital twin applications. Customer use cases and SmartUQ’s ability to integrate with ANSYS will also be demonstrated and discussed.