Inferring bifurcation diagrams of two distinct chaotic systems by a single machine
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918474364747776 |
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| author | Guo, Jianmin Du, Yao Yu, Yizhen Zou, Yong Wang, Xingang |
| author_facet | Guo, Jianmin Du, Yao Yu, Yizhen Zou, Yong Wang, Xingang |
| contents | We propose a dual-channel reservoir-computing scheme for inferring the dynamics of two distinct chaotic systems with a single machine. By augmenting a standard reservoir with a system-label channel and a parameter-control channel, the machine can be trained from time series collected from a few sampled states of the two systems. We show that the trained machine not only predicts the short-time evolution of the sampled states, but also reproduces the long-term statistical properties of unseen states, thereby enabling reconstruction of the bifurcation diagrams of both systems from partial observations. The effectiveness of the scheme is demonstrated for the Lorenz and Rössler systems in numerical simulations and for the Chua and Rossler circuits in experiments. Functional-network analysis further shows that the two target systems are encoded by distinct dynamical patterns in the reservoir. These results extend multifunctional and parameter-aware reservoir computing, and provide a route to data-driven inference of multiple nonlinear systems using a single machine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26632 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inferring bifurcation diagrams of two distinct chaotic systems by a single machine Guo, Jianmin Du, Yao Yu, Yizhen Zou, Yong Wang, Xingang Chaotic Dynamics Machine Learning We propose a dual-channel reservoir-computing scheme for inferring the dynamics of two distinct chaotic systems with a single machine. By augmenting a standard reservoir with a system-label channel and a parameter-control channel, the machine can be trained from time series collected from a few sampled states of the two systems. We show that the trained machine not only predicts the short-time evolution of the sampled states, but also reproduces the long-term statistical properties of unseen states, thereby enabling reconstruction of the bifurcation diagrams of both systems from partial observations. The effectiveness of the scheme is demonstrated for the Lorenz and Rössler systems in numerical simulations and for the Chua and Rossler circuits in experiments. Functional-network analysis further shows that the two target systems are encoded by distinct dynamical patterns in the reservoir. These results extend multifunctional and parameter-aware reservoir computing, and provide a route to data-driven inference of multiple nonlinear systems using a single machine. |
| title | Inferring bifurcation diagrams of two distinct chaotic systems by a single machine |
| topic | Chaotic Dynamics Machine Learning |
| url | https://arxiv.org/abs/2604.26632 |