Inverse problems for history-enriched linear model reduction

TL;DR

This paper develops exact history-enriched models for linear dynamical systems using Mori-Zwanzig theory, formulates inverse problems to learn model operators from data, and demonstrates accurate reconstruction and prediction of system dynamics.

Contribution

It introduces a novel approach to incorporate history dependence into linear model reduction and provides methods for operator identification from data, addressing partial and full observations.

Findings

Operators are identifiable with full data under mild assumptions.

Unique solutions exist for partial data only if the full-state operator is time-invariant.

Learned models accurately reconstruct and predict system trajectories.

Abstract

Standard projection-based model reduction for dynamical systems incurs closure error because it only accounts for instantaneous dependence on the resolved state. From the Mori-Zwanzig (MZ) perspective, projecting the full dynamics onto a low-dimensional resolved subspace induces additional noise and memory terms arising from the dynamics of the unresolved component in the orthogonal complement. The memory term makes the resolved dynamics explicitly history dependent. In this work, based on the MZ identity, we derive exact, history-enriched models for the resolved dynamics of linear driven dynamical systems and formulate inverse problems to learn model operators from discrete snapshot data via least-squares regression. We propose a greedy time-marching scheme to solve the inverse problems efficiently and analyze operator identifiability under full and partial observation data availability. For full observation data, we show that, under mild assumptions, the operators are identifiable even when the full-state dynamics are governed by a general time-varying linear operator, whereas with partial observation data the inverse problem has a unique solution only when the full-state operator is time-invariant. To address the resulting non-uniqueness in the time-varying case, we introduce a time-smoothing Tikhonov regularization. Numerical results demonstrate that the operators can be faithfully reconstructed from both full and partial observation data and that the learned history-enriched MZ models yield accurate trajectories of the resolved state.