Robust information divergences for model-form uncertainty arising from sparse data in random PDE
Provides a non-intrusive, data-informed uncertainty quantification tool for regulatory and risk management decisions in subsurface flow modeling.
The paper develops hybrid information divergences for quantifying model-form uncertainty in subsurface flow problems with sparse data, demonstrating uncertainty bounds for parametric sensitivity and model misspecification.
We develop a novel application of hybrid information divergences to analyze uncertainty in steady-state subsurface flow problems. These hybrid information divergences are non-intrusive, goal-oriented uncertainty quantification tools that enable robust, data-informed predictions in support of critical decision tasks such as regulatory assessment and risk management. We study the propagation of model-form or epistemic uncertainty with numerical experiments that demonstrate uncertainty quantification bounds for (i) parametric sensitivity analysis and (ii) model misspecification due to sparse data. Further, we make connections between the hybrid information divergences and certain concentration inequalities that can be leveraged for efficient computing and account for any available data through suitable statistical quantities.