Calibrating Digital Twins via Bayesian Optimization with a Root Finding Strategy

Calibrating digital twins is a challenging task that various methodologies have addressed. Bayesian Optimization is a prominent tool for this purpose albeit with computational limitations. We propose root-finding strategies within the Bayesian Optimization framework that is tailored for digital twin calibration task. Employing root-finding scheme introduces new acquisition functions and offer unique advantages over traditional minimization strategy, particularly when using a continuous surrogate model. We demonstrate our findings through a range of motivating examples and calibration tasks.