Uncertainty Quantification with Model Structural Error


Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy, is often the largest contributor to the overall predictive uncertainty. This talk will overview the current state of model error estimation methods, focusing on embedded model error estimation. Namely, I will present a Bayesian inference framework for representing, quantifying, and propagating uncertainties due to model structural errors by embedding stochastic correction terms in the model. The embedded correction approach ensures physical constraints are satisfied, and renders calibrated model predictions meaningful and robust with respect to structural errors over multiple, even unobservable, quantities of interest. Key challenges and strengths of this method will be demonstrated on both synthetic examples and practical engineering applications.

Sep 4, 2020
Summer School Sapienza University of Rome
(virtual) Rome