Sequential Maximal Updated Density Parameter Estimation for Dynamical Systems with Parameter Drift

TL;DR

This paper introduces a new sequential parameter estimation method within a data-consistent framework for dynamical systems, capable of real-time updates and detecting parameter drift using spatio-temporal data.

Contribution

It extends the data-consistent framework with algorithms for real-time maximal updated density (MUD) estimation, enabling detection of parameter drift in dynamical systems.

Findings

Successfully estimated wind drag parameters in storm surge models

Estimated thermal diffusivity fields in heat conduction problems

Tracked changing infection and incubation rates in epidemiological models

Abstract

We present a novel method for generating sequential parameter estimates and quantifying epistemic uncertainty in dynamical systems within a data-consistent (DC) framework. The DC framework differs from traditional Bayesian approaches due to the incorporation of the push-forward of an initial density, which performs selective regularization in parameter directions not informed by the data in the resulting updated density. This extends a previous study that included the linear Gaussian theory within the DC framework and introduced the maximal updated density (MUD) estimate as an alternative to both least squares and maximum a posterior (MAP) estimates. In this work, we introduce algorithms for operational settings of MUD estimation in real or near-real time where spatio-temporal datasets arrive in packets to provide updated estimates of parameters and identify potential parameter drift. Computational diagnostics within the DC framework prove critical for evaluating (1) the quality of the DC update and MUD estimate and (2) the detection of parameter value drift. The algorithms are applied to estimate (1) wind drag parameters in a high-fidelity storm surge model, (2) thermal diffusivity field for a heat conductivity problem, and (3) changing infection and incubation rates of an epidemiological model.