Control in the spaces of ensembles of points
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Abstract (arXiv)
We study the controlled dynamics of the it ensembles of points of a Riemannian manifold M. Parameterized ensemble of points of M is the image of a continuous map γ:Theta → M, where Theta is a compact set of parameters. The dynamics of ensembles is defined by the action γ(θ) mapsto Pt(γ(θ)) of the semigroup of diffeomorphisms Pt:M → M, t ∈ ℝ, generated by the controlled equation dotx=f(x,u(t)) on M. Therefore any control system on M defines a control system on (generally infinite-dimensional) space E_Theta(M) of the ensembles of points. We wish to establish criteria of controllability for such control systems. As in our previous work ([1]) we seek to adapt the Lie-algebraic approach of geometric control theory to the infinite-dimensional setting. We study the case of finite ensembles and prove genericity of exact controllability property for them. We also find sufficient approximate controllability criterion for continual ensembles and prove a result on motion planning in the space of flows on M. We discuss the relation of the obtained controllability criteria to various versions of Rashevsky-Chow theorem for finite- and infinite-dimensional manifolds.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 2; 2026: 2.
8 papers.
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- 2021Deep Learning Approximation of Diffeomorphisms via Linear-Control Systemscited by 7
- 2024Control Theoretic Approach to Fine-Tuning and Transfer Learningcited by 3
- 2023Normalizing flows as approximations of optimal transport maps via linear-control neural ODEscited by 2
- 2025Geometric Foundations of Tuning without Forgetting in Neural ODEscited by 2
- 2025Control Disturbance Rejection in Neural ODEscited by 1
- 2026Constructive interpolation and generalization rates for neural ODEs: a control perspectivecited by 0
- 2026Privacy Preserving Gossip Learningcited by 0
- 2026Privacy Preserving Gossip Learning13 Sep 2026
- 2026Constructive interpolation and generalization rates for neural ODEs: a control perspective30 May 2026
- 2025Control Disturbance Rejection in Neural ODEs22 Sep 2025
- 2025Geometric Foundations of Tuning without Forgetting in Neural ODEs3 Sep 2025
- 2024Measure-to-measure interpolation using Transformers7 Nov 2024
- 2024Control Theoretic Approach to Fine-Tuning and Transfer Learning17 Apr 2024
- 2023Normalizing flows as approximations of optimal transport maps via linear-control neural ODEs2 Nov 2023
- 2021Deep Learning Approximation of Diffeomorphisms via Linear-Control Systems24 Oct 2021