Webinars

As part of a shift toward digital engineering, digital twins are increasingly used by industries including aerospace, defense, automotive, industrial machinery, and semiconductor. Many companies have allocated large amounts of funding to start digital twin programs. For these programs to be successful digital twins must be accurate and reliable representations of physical reality. Therefore, a key digital twin challenge is to close the gap between the virtual model (the twin) and real-world observations such as from tests or sensor data. SmartUQ features frequentist and Bayesian statistical calibration tools which can leverage machine learning to better correlate predictions from the virtual with the physical and close the gap. Join us for his webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce SmartUQ’s statistical calibration tools in the context of digital twin applications.