Dynamic models with $p$ parameters are identified by $2p+1$ random features

Michael Wieck-Sosa; Cosma Rohilla Shalizi

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arXiv · 2607.16035

Dynamic models with $p$ parameters are identified by $2p+1$ random features

Michael Wieck-Sosa·Cosma Rohilla Shalizi

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Abstract

A foundational principle in nonlinear dynamics is that the structure of a dynamical system can be recovered from a small number of generic measurements or coordinates. We develop an analogous principle for the identification of dynamic models for time series with noise, which builds on previous identification results for noiseless dynamical systems. The noise is allowed to be non-iid, non-Gaussian, and dependent on the state. Our results cover noisily observed differential equations and discrete-time dynamical systems, as well as stochastic models with process noise. We illustrate the utility of this identification principle using a Lorenz-63 model and a H\'{e}non map model, both with observational noise.

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Michael Wieck-Sosa, Cosma Rohilla Shalizi. 2026-07-17. Dynamic models with $p$ parameters are identified by $2p+1$ random features. https://arxiv.org/abs/2607.16035

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