Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems
Chaotic Dynamics
2026-04-10 v2
Abstract
We introduce a computationally efficient and accurate reduced order modelling approach for the optimization of spatiotemporally chaotic systems. The proposed method combines quantized local reduced order modelling with adjoint-based optimization. We employ the methodology in a variational data assimilation problem for the chaotic Kuramoto-Sivashinsky equation and show that it successfully reconstructs the full trajectory for up to 0.25 Lyapunov times given full state measurements at the final time. The proposed algorithm provides 3.5 times speed-up when compared to the full-order model. The proposed method opens up new possibilities for the reduced order modelling of spatiotemporally chaotic systems.
Cite
@article{arxiv.2603.05531,
title = {Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems},
author = {Defne E. Ozan and Antonio Colanera and Luca Magri},
journal= {arXiv preprint arXiv:2603.05531},
year = {2026}
}