Webinars

Getting the most out of COMSOL 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 COMSOL 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 COMSOL simulation. This ML model is then used in place of the COMSOL simulation to run any desired analyses. The ML model’s rapid prediction of the COMSOL simulation results allows more analyses to be performed and in less time. COMSOL simulations and surrogate models of COMSOL simulations are of course only of benefit if the results they produce agree well with physical data, for example in the form of test or experimental data. This 3rd of 4 webinars will cover SmartUQ’s tools for model calibration including unique statistical calibration approaches which address the role that both parameter uncertainty and modeling assumptions play in the disagreement between simulation results and physical data.