Webinars

Engineering simulations rarely match physical test results perfectly. Differences can arise from uncertainty in model parameters, but also from limitations in the simulation model itself, such as simplifying assumptions or incomplete physics. Traditional calibration approaches focus on adjusting model parameters to improve agreement with test data, but parameter tuning alone cannot adequately address all sources of disagreement. Statistical calibration provides a better, more comprehensive approach by simultaneously calibrating uncertain model parameters and quantifying discrepancies between simulation and physical measurements. By accounting for both parameter uncertainty and model discrepancy, statistical calibration can help engineers better understand the sources of disagreement, reduce prediction errors, improve correlation with test data, and more accurately characterize uncertainty in model predictions. Join this webinar to learn how SmartUQ's advanced statistical calibration capabilities can help engineers improve the accuracy and reliability of engineering simulations. Customer use cases and demonstrations will be included as part of the presentation.