Unsupervised learning for anticipating critical transitions

• URL: http://arxiv.org/abs/2501.01579v1
• Date: Thu, 02 Jan 2025 23:57:23 GMT
• Title: Unsupervised learning for anticipating critical transitions
• Authors: Shirin Panahi, Ling-Wei Kong, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai,
• Abstract summary: We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, the driving factor is detected from time series using the VAE in an unsupervised-learning fashion. We demonstrate power of prototypical unsupervised learning scheme using dynamical systems including the Kuramoto-Sivashinsky system.
• Score: 0.249660468924754
• License:
• Abstract: For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge of the bifurcation parameter. We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, the driving factor is detected from time series using the VAE in an unsupervised-learning fashion and the extracted information is then used as the parameter input to the reservoir computer for anticipating the critical transition. We demonstrate the power of the unsupervised learning scheme using prototypical dynamical systems including the spatiotemporal Kuramoto-Sivashinsky system. The scheme can also be extended to scenarios where the target system is driven by several independent parameters or with partial state observations.

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