ArticleChaos (Woodbury, N.Y.)2024
Data-driven control of oscillator networks with population-level measurement.
Article in Chaos (Woodbury, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Control of Oscillator Networks with Mean-Field Measurement: A Hybrid Open/Closed-Loop Approach.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2026Article
- Data-Driven Pattern Formation in Oscillator Networks Using Partial Observations.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2025Article
- Magnitude-constrained optimal chaotic desynchronization of neural populations.Frontiers in network physiology · 2025Article
- A framework for optimal control of oscillations and synchrony applied to non-linear models of neural population dynamics.Frontiers in computational neuroscience · 2024Article
- Data-Efficient Inference of Nonlinear Oscillator Networks.IFAC-PapersOnLine · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Controlling complex networks of nonlinear limit-cycle oscillators is an important problem pertinent to various applications in engineering and natural sciences. While in recent years the control of oscillator populations with comprehensive biophysical models or simplified models, e.g., phase models, has seen notable advances, learning appropriate controls directly from data without prior model assumptions or pre-existing data remains a challenging and less developed area of research. In this paper, we address this problem by leveraging the network's current dynamics to iteratively learn an appropriate control online without constructing a global model of the system. We illustrate through a range of numerical simulations that the proposed technique can effectively regulate synchrony in various oscillator networks after a small number of trials using only one input and one noisy population-level output measurement. We provide a theoretical analysis of our approach, illustrate its robustness to system variations, and compare its performance with existing model-based and data-driven approaches.
Identifiers
38526979What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.