Mori-Zwanzig reduced models for uncertainty quantification II: Initial condition uncertainty

TL;DR

This paper develops a Mori-Zwanzig based method to create reduced models for systems with uncertain initial conditions, including an on-the-fly parameter estimation algorithm demonstrated on the viscous Burgers equation.

Contribution

It introduces a novel algorithm for estimating memory parameters in reduced models with uncertain initial conditions, enabling efficient simulation.

Findings

The algorithm accurately estimates parameters during initial full-system evolution.

Reduced models with estimated parameters match full system dynamics.

Demonstrated effectiveness on viscous Burgers equation with uncertain initial conditions.

Abstract

In a recent preprint (arXiv:1211.4285v1) we addressed the problem of constructing reduced models for time-dependent systems described by differential equations which involve uncertain parameters. In the current work, we focus on the construction of reduced models for systems of differential equations when the initial condition is uncertain. While for both cases the reduced models are constructed through the Mori-Zwanzig formalism, the necessary estimation of the memory parameters is quite different. For the case of uncertain initial conditions we present an algorithm which allows to estimate on the fly the parameters appearing in the reduced model. The first part of the algorithm evolves the full system until the estimation of the parameters for the reduced model has converged. At the time instant that this happens, the algorithm switches to the evolution of only the reduced model with the estimated parameter values from the first part of the algorithm. The viscous Burgers equation with uncertain initial condition is used to illustrate the construction.