Meng-Wu Guo

Active Learning for Data-Driven Reduced Models of Parametric Differential Systems with Bayesian Operator Inference

This work establishes a probabilistic version of parametric operator inference, casting the learning problem as a Bayesian linear regression, and designs a sequential adaptive sampling scheme to select new training parameter vectors that promote ROM stability and accuracy globally in the parameter domain.