mlsanityData to 7 Oct 2026

Designing Chaotic Attractors: A Semi-supervised Approach

arXiv 2407.09545 ↗First posted 27 Jun 2024cs.NE cs.LG math.DScited by 3

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Abstract (arXiv)

Chaotic dynamics are ubiquitous in nature and useful in engineering, but their geometric design can be challenging. Here, we propose a method using reservoir computing to generate chaos with a desired shape by providing a periodic orbit as a template, called a skeleton. We exploit a bifurcation of the reservoir to intentionally induce unsuccessful training of the skeleton, revealing inherent chaos. The emergence of this untrained attractor, resulting from the interaction between the skeleton and the reservoir's intrinsic dynamics, offers a novel semi-supervised framework for designing chaos.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

11 of 40 references.

References · 11

  1. 2021On the difficulty of learning chaotic dynamics with RNNscited by 64
  2. 2020Physical reservoir computing -- An introductory perspectivecited by 58
  3. 2020Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Functioncited by 26
  4. 2020Multifunctionality in a Reservoir Computercited by 21
  5. 2020Designing spontaneous behavioral switching via chaotic itinerancycited by 13
  6. 2023Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstructioncited by 9
  7. 2023Embedding bifurcations into pneumatic artificial musclecited by 7
  8. 2019Optimal short-term memory before the edge of chaos in driven random recurrent networkscited by 6
  9. 2020Inferring Global Dynamics Using a Learning Machinecited by 4
  10. 2022Creating New Chaotic Signals with Reservoir Computerscited by 3
  11. 2024Exploiting Chaotic Dynamics as Deep Neural Networkscited by 2

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.