Data-Driven Pattern Formation in Oscillator Networks Using Partial Observations
Yi-Hsuan Shih1, Bharat Singhal1, Jr-Shin Li1
1Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis MO, USA.
Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control
|June 8, 2026
Summary
This study introduces a novel data-driven control framework for oscillator networks. It enables arbitrary synchronization patterns without needing to measure every element, advancing control applications.
Area of Science:
- Complex systems
- Network science
- Control theory
Background:
- Effective control of oscillator networks is crucial for neuroscience, circadian biology, and engineering.
- Data-driven control is emerging due to the lack of accurate dynamical models, but current methods have limitations.
- Existing methods often require measuring individual elements and achieve only simple binary patterns like synchronization/desynchronization.
Purpose of the Study:
- To develop a data-driven control framework for oscillator populations.
- To achieve arbitrary synchronization patterns without measuring all network elements.
- To overcome limitations of current data-driven control approaches.
Main Methods:
- Characterizing network synchronization patterns using order parameters.
- Formulating the control task as a stochastic optimization problem.
- Solving the optimization problem using stochastic gradient descent.
Main Results:
- Demonstrated effectiveness in forming diverse synchronization patterns.
- Successfully applied to both simplified phase models and biophysical neuron models.
- Achieved arbitrary synchronization patterns without full network measurement.
Conclusions:
- The proposed framework offers a powerful new method for controlling oscillator networks.
- This approach expands the practical applicability of data-driven control in complex systems.
- Enables precise control over network dynamics for various scientific and engineering fields.
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