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 series will discuss the role design of experiments, surrogate modeling, and machine learning (ML) tools such as statistical calibration can play in calibrating ANSYS simulations to physical data, validating their accuracy, and maximizing the knowledge gained from their use. With surrogate modeling, first an ML model is trained to predict the results of a particular ANSYS simulation. This ML model is then used in place of the ANSYS simulation to run any desired analyses. The ML model’s rapid prediction of the ANSYS simulation results allows more analyses to be performed and in less time. In this last of 4 webinars the use of SmartUQ’s sensitivity analysis tools for gaining insights into ANSYS simulations will be discussed. Demonstrations of sensitivity analysis in SmartUQ will illustrate applications including assessing how robust the design or process being modeled is to uncertainty and which ANSYS model inputs are the greatest drivers of uncertainty in the outputs.