A Stochastic Operator Approach to Model Inadequacy with Applications to Contaminant Transport

TL;DR

This paper develops a stochastic operator framework to quantify and incorporate model inadequacy in physical predictions, especially for contaminant transport, by inferring infinite-dimensional operators and analyzing high-fidelity models.

Contribution

It introduces a novel stochastic operator approach for representing model inadequacy, including methods for inferring infinite-dimensional operators and interrogating high-fidelity models.

Findings

Framework for inferring infinite-dimensional operators

Method for interrogating high-fidelity models

Enhanced uncertainty quantification in contaminant transport

Abstract

The mathematical models used to represent physical phenomena are generally known to be imperfect representations of reality. Model inadequacies arise for numerous reasons, such as incomplete knowledge of the phenomena or computational intractability of more accurate models. In such situations it is impractical or impossible to improve the model, but necessity requires its use to make predictions. With this in mind, it is important to represent the uncertainty that a model's inadequacy causes in its predictions, as neglecting to do so can cause overconfidence in its accuracy. A powerful approach to addressing model inadequacy leverages the composite nature of physical models by enriching a flawed embedded closure model with a stochastic error representation. This work outlines steps in the development of a stochastic operator as an inadequacy representation by establishing the framework for inferring an infinite-dimensional operator and by introducing a novel method for interrogating available high-fidelity models to learn about modeling error.