Online model learning with data-assimilated reservoir computers

• URL: http://arxiv.org/abs/2504.16767v1
• Date: Wed, 23 Apr 2025 14:35:54 GMT
• Title: Online model learning with data-assimilated reservoir computers
• Authors: Andrea Nóvoa, Luca Magri,
• Abstract summary: We propose an online learning framework for forecasting nonlinear-temporal signals (fields)<n>We demonstrate the framework on a wake past a cylinder governed by the Navier-Stokes equations.
• Score: 4.266376725904727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an online learning framework for forecasting nonlinear spatio-temporal signals (fields). The method integrates (i) dimensionality reduction, here, a simple proper orthogonal decomposition (POD) projection; (ii) a generalized autoregressive model to forecast reduced dynamics, here, a reservoir computer; (iii) online adaptation to update the reservoir computer (the model), here, ensemble sequential data assimilation.We demonstrate the framework on a wake past a cylinder governed by the Navier-Stokes equations, exploring the assimilation of full flow fields (projected onto POD modes) and sparse sensors. Three scenarios are examined: a na\"ive physical state estimation; a two-fold estimation of physical and reservoir states; and a three-fold estimation that also adjusts the model parameters. The two-fold strategy significantly improves ensemble convergence and reduces reconstruction error compared to the na\"ive approach. The three-fold approach enables robust online training of partially-trained reservoir computers, overcoming limitations of a priori training. By unifying data-driven reduced order modelling with Bayesian data assimilation, this work opens new opportunities for scalable online model learning for nonlinear time series forecasting.

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