Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning

• URL: http://arxiv.org/abs/2505.24099v1
• Date: Fri, 30 May 2025 01:01:09 GMT
• Title: Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
• Authors: Mohammad Shah Alam, William Ott, Ilya Timofeyev,
• Abstract summary: In the paper, we explore the predictive capabilities of echo state networks (ESNs) for the generalized Kuramoto-Sshinsky (gKS) equation.<n>We introduce a novel methodology that integrates ESNs with transfer learning to enhance predictive performance across various parameter regimes of the gKS model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore the predictive capabilities of echo state networks (ESNs) for the generalized Kuramoto-Sivashinsky (gKS) equation, an archetypal nonlinear PDE that exhibits spatiotemporal chaos. We introduce a novel methodology that integrates ESNs with transfer learning, aiming to enhance predictive performance across various parameter regimes of the gKS model. Our research focuses on predicting changes in long-term statistical patterns of the gKS model that result from varying the dispersion relation or the length of the spatial domain. We use transfer learning to adapt ESNs to different parameter settings and successfully capture changes in the underlying chaotic attractor.

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