# Designing Chaotic Attractors: A Semi-supervised Approach

arXiv: https://arxiv.org/abs/2407.09545 · HTML: https://mlsanity.com/paper/2407.09545/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [On the difficulty of learning chaotic dynamics with RNNs](https://mlsanity.com/paper/2110.07238/) (2021)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function](https://mlsanity.com/paper/2002.06189/) (2020)
- [Multifunctionality in a Reservoir Computer](https://mlsanity.com/paper/2008.06348/) (2020)
- [Designing spontaneous behavioral switching via chaotic itinerancy](https://mlsanity.com/paper/2002.08332/) (2020)
- [Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstruction](https://mlsanity.com/paper/2401.00885/) (2023)
- [Embedding bifurcations into pneumatic artificial muscle](https://mlsanity.com/paper/2305.03994/) (2023)
- [Optimal short-term memory before the edge of chaos in driven random recurrent networks](https://mlsanity.com/paper/1912.11213/) (2019)
- [Inferring Global Dynamics Using a Learning Machine](https://mlsanity.com/paper/2009.13032/) (2020)
- [Creating New Chaotic Signals with Reservoir Computers](https://mlsanity.com/paper/2210.06250/) (2022)
- [Exploiting Chaotic Dynamics as Deep Neural Networks](https://mlsanity.com/paper/2406.02580/) (2024)

## Built on by (most cited first, 3 of 3)

- [Multifunctional physical reservoir computing in soft tensegrity robots](https://mlsanity.com/paper/2507.21496/) (2025)
- [Reservoir Computing Generalized](https://mlsanity.com/paper/2412.12104/) (2024)
- [Attractor-merging Crises and Intermittency in Reservoir Computing](https://mlsanity.com/paper/2504.12695/) (2025)

## 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.