Representing model inadequacy: A stochastic operator approach
Rebecca E Morrison, Todd A Oliver, Robert D Moser

TL;DR
This paper introduces a probabilistic, physically consistent stochastic operator to represent model inadequacy in chemical kinetics, enabling better uncertainty quantification and calibration within ODE-based models.
Contribution
It proposes a novel stochastic inadequacy operator embedded in ODEs, respecting physical constraints, and calibrated via hierarchical Bayesian inference for chemical kinetics models.
Findings
Effective representation of model discrepancy in hydrogen combustion
Probabilistic calibration improves predictive accuracy
Maintains physical laws like conservation during modeling
Abstract
Mathematical models of physical systems are subject to many uncertainties such as measurement errors and uncertain initial and boundary conditions. After accounting for these uncertainties, it is often revealed that discrepancies between the model output and the observations remain; if so, the model is said to be inadequate. In practice, the inadequate model may be the best that is available or tractable, and so despite its inadequacy the model may be used to make predictions of unobserved quantities. In this case, a representation of the inadequacy is necessary, so the impact of the observed discrepancy can be determined. We investigate this problem in the context of chemical kinetics and propose a new technique to account for model inadequacy that is both probabilistic and physically meaningful. A stochastic inadequacy operator is introduced which is embedded in the ODEs…
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