Embedding Information onto a Dynamical System
Not on the map. Why?
Abstract (arXiv)
The celebrated Takens' embedding theorem concerns embedding an attractor of a dynamical system in a Euclidean space of appropriate dimension through a generic delay-observation map. The embedding also establishes a topological conjugacy. In this paper, we show how an arbitrary sequence can be mapped into another space as an attractive solution of a nonautonomous dynamical system. Such mapping also entails a topological conjugacy and an embedding between the sequence and the attractive solution spaces. This result is not a generalization of Takens embedding theorem but helps us understand what exactly is required by discrete-time state space models widely used in applications to embed an external stimulus onto its solution space. Our results settle another basic problem concerning the perturbation of an autonomous dynamical system. We describe what exactly happens to the dynamics when exogenous noise perturbs continuously a local irreducible attracting set (such as a stable fixed point) of a discrete-time autonomous dynamical system.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 3 of its 34 references that are in the map. First posted on arXiv 22 May 2021.
Builds on
3 of 34 references.
References · 3
- 2018Echo state networks are universalcited by 87
- 2020Discrete-time signatures and randomness in reservoir computingcited by 23
- 2019Differentiable reservoir computingcited by 20
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.
5 papers.
- 2023Infinite-dimensional reservoir computingcited by 7
- 2024State-space systems as dynamic generative modelscited by 3
- 2025Stochastic dynamics learning with state-space systemscited by 3
- 2025Echoes of the Past: A Unified Perspective on Fading memory and Echo Statescited by 2
- 2026Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computingcited by 0
- 2026Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing24 May 2026
- 2025Echoes of the Past: A Unified Perspective on Fading memory and Echo States26 Aug 2025
- 2025Stochastic dynamics learning with state-space systems11 Aug 2025
- 2024State-space systems as dynamic generative models12 Apr 2024
- 2023Infinite-dimensional reservoir computing2 Apr 2023
Similar, not cited
Close in wording, no citation either way
- 2024Delay Embedding Theory of Neural Sequence Models
- 2026Cochain Perspectives on Temporal-Difference Signals for Learning Beyond Markov Dynamics
- 2025Observability conditions for neural state-space models with eigenvalues and their roots of unity
- 2026Stochastic Dynamics on Persistence Diagram Space via Reinforcement Learning
- 2025Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures
- 2026Persistent Cross Entropy
- 2025Dynamical symmetries in the fluctuation-driven regime: an application of Noether's theorem to noisy dynamical systems
- 2023Discrete, compositional, and symbolic representations through attractor dynamics