Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing

• URL: http://arxiv.org/abs/2506.05292v1
• Date: Thu, 05 Jun 2025 17:46:07 GMT
• Title: Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
• Authors: Declan A. Norton, Yuanzhao Zhang, Michelle Girvan,
• Abstract summary: Reservoir computing can generalize to unexplored regions of state space without explicit structural priors.<n>We show that RCs trained on trajectories from a single basin of attraction can achieve out-of-domain generalization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning techniques offer an effective approach to modeling dynamical systems solely from observed data. However, without explicit structural priors -- built-in assumptions about the underlying dynamics -- these techniques typically struggle to generalize to aspects of the dynamics that are poorly represented in the training data. Here, we demonstrate that reservoir computing -- a simple, efficient, and versatile machine learning framework often used for data-driven modeling of dynamical systems -- can generalize to unexplored regions of state space without explicit structural priors. First, we describe a multiple-trajectory training scheme for reservoir computers that supports training across a collection of disjoint time series, enabling effective use of available training data. Then, applying this training scheme to multistable dynamical systems, we show that RCs trained on trajectories from a single basin of attraction can achieve out-of-domain generalization by capturing system behavior in entirely unobserved basins.

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