Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
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Abstract (arXiv)
While invariant measures are widely employed to analyze physical systems when a direct study of pointwise trajectories is intractable, e.g., due to chaos or noise, they cannot uniquely identify the underlying dynamics. Our first result shows that, in contrast to invariant measures in state coordinates, e.g., [x(t), y(t), z(t)], the invariant measure expressed in time-delay coordinates, e.g., [x(t), x(t-τ),ldots, x(t-(m-1)τ)], can identify the dynamics up to a topological conjugacy. Our second result resolves the remaining ambiguity: by combining invariant measures constructed from multiple delay frames with distinct observables, the system is uniquely identifiable, provided that a suitable initial condition is satisfied. These guarantees require informative observables and appropriate delay parameters (m,τ), which can be limiting in certain settings. We support our theoretical contributions through a series of physical examples demonstrating how invariant measures expressed in delay-coordinates can be used to perform robust system identification in practice.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 47 references that are in the map. First posted on arXiv 30 Nov 2024.
Builds on
11 of 47 references.
References · 11
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- 2022Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systemscited by 50
- 2023Training neural operators to preserve invariant measures of chaotic attractorscited by 46
- 2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systemscited by 30
- 2024LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensemblescited by 30
- 2024When are dynamical systems learned from time series data statistically accurate?cited by 15
- 2021Optimal Transport for Parameter Identification of Chaotic Dynamics via Invariant Measurescited by 10
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- 2024Learning Dissipative Chaotic Dynamics with Boundedness Guaranteescited by 5
- 2021Optimal linear response for Markov Hilbert-Schmidt integral operators and stochastic dynamical systemscited by 1
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Citing papers per year: 2026: 5.
5 papers.
- 2026Transformers for dynamical systems learn transfer operators in-contextcited by 2
- 2026The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modelingcited by 1
- 2026The Dynamics of Discoverability: How Trajectories and Priors Shape Equation Recoverycited by 1
- 2026Goal-oriented learning of stochastic differential equations using error bounds on path-space observablescited by 0
- 2026Conditional Score-Based Modeling of Effective Langevin Dynamicscited by 0
- 2026The Dynamics of Discoverability: How Trajectories and Priors Shape Equation Recovery20 Jul 2026
- 2026The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling29 May 2026
- 2026Conditional Score-Based Modeling of Effective Langevin Dynamics27 Apr 2026
- 2026Goal-oriented learning of stochastic differential equations using error bounds on path-space observables20 Mar 2026
- 2026Transformers for dynamical systems learn transfer operators in-context21 Feb 2026