Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
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Abstract (arXiv)
Bifurcations in dynamical systems characterize qualitative changes in the system behavior. Therefore, their detection is important because they can signal the transition from normal system operation to imminent failure. While standard persistent homology has been used in this setting, it usually requires analyzing a collection of persistence diagrams, which in turn drives up the computational cost considerably. Using zigzag persistence, we can capture topological changes in the state space of the dynamical system in only one persistence diagram. Here we present Bifurcations using ZigZag (BuZZ), a one-step method to study and detect bifurcations using zigzag persistence. The BuZZ method is successfully able to detect this type of behavior in two synthetic examples as well as an example dynamical system.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 3.
7 papers.
- 2024Persistent Topological Features in Large Language Modelscited by 11
- 2022Persistent Homology of Coarse Grained State Space Networkscited by 2
- 2023Let's do the time-warp-attend: Learning topological invariants of dynamical systemscited by 2
- 2024Neural Persistence Dynamicscited by 0
- 2026Topological Detection of Hopf Bifurcations via Persistent Homology: A Functional Criterion from Time Seriescited by 0
- 2026Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embeddingcited by 0
- 2026A differentiability framework for zigzag persistent homology via linear interpolationcited by 0
- 2026A differentiability framework for zigzag persistent homology via linear interpolation30 Sep 2026
- 2026Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding8 Sep 2026
- 2026Topological Detection of Hopf Bifurcations via Persistent Homology: A Functional Criterion from Time Series28 Mar 2026
- 2024Persistent Topological Features in Large Language Models14 Oct 2024
- 2024Neural Persistence Dynamics24 May 2024
- 2023Let's do the time-warp-attend: Learning topological invariants of dynamical systems14 Dec 2023
- 2022Persistent Homology of Coarse Grained State Space Networks20 May 2022