Topological State Space Inference for Dynamical Systems

Mishal Assif P K, Yuliy Baryshnikov
Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, PMLR 283:1206-1216, 2025.

Abstract

We present a computational pipe aiming at recovery of the topology of the underlying phase space from observation of an output function along a sample of trajectories of a dynamical system.

Cite this Paper

BibTeX
@InProceedings{pmlr-v283-k25a, title = {Topological State Space Inference for Dynamical Systems}, author = {K, Mishal Assif P and Baryshnikov, Yuliy}, booktitle = {Proceedings of the 7th Annual Learning for Dynamics \& Control Conference}, pages = {1206--1216}, year = {2025}, editor = {Ozay, Necmiye and Balzano, Laura and Panagou, Dimitra and Abate, Alessandro}, volume = {283}, series = {Proceedings of Machine Learning Research}, month = {04--06 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v283/main/assets/k25a/k25a.pdf}, url = {https://proceedings.mlr.press/v283/k25a.html}, abstract = {We present a computational pipe aiming at recovery of the topology of the underlying phase space from observation of an output function along a sample of trajectories of a dynamical system.} }
Endnote
%0 Conference Paper %T Topological State Space Inference for Dynamical Systems %A Mishal Assif P K %A Yuliy Baryshnikov %B Proceedings of the 7th Annual Learning for Dynamics \& Control Conference %C Proceedings of Machine Learning Research %D 2025 %E Necmiye Ozay %E Laura Balzano %E Dimitra Panagou %E Alessandro Abate %F pmlr-v283-k25a %I PMLR %P 1206--1216 %U https://proceedings.mlr.press/v283/k25a.html %V 283 %X We present a computational pipe aiming at recovery of the topology of the underlying phase space from observation of an output function along a sample of trajectories of a dynamical system.
APA
K, M.A.P. & Baryshnikov, Y.. (2025). Topological State Space Inference for Dynamical Systems. Proceedings of the 7th Annual Learning for Dynamics \& Control Conference, in Proceedings of Machine Learning Research 283:1206-1216 Available from https://proceedings.mlr.press/v283/k25a.html.

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