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• W4224018082 abstract "Modelling biological or engineering swarms is challenging due to the inherently high dimension of the system, despite the often low-dimensional emergent dynamics. Most existing swarm modelling approaches are based on first principles and often result in swarm-specific parameterizations that do not generalize to a broad range of applications. In this work, we apply a purely data-driven method to (1) learn local interactions of homogeneous swarms through observation data and to (2) generate similar swarming behaviour using the learned model. In particular, a modified version of dynamic mode decomposition with control, called swarmDMD, is developed and tested on the canonical Vicsek swarm model. The goal is to use swarmDMD to learn inter-agent interactions that give rise to the observed swarm behaviour. We show that swarmDMD can faithfully reconstruct the swarm dynamics, and the model learned by swarmDMD provides a short prediction window for data extrapolation with a trade-off between prediction accuracy and prediction horizon. We also provide a comprehensive analysis on the efficacy of different observation data types on the modelling, where we find that inter-agent distance yields the most accurate models. We believe the proposed swarmDMD approach will be useful for studying multi-agent systems found in biology, physics, and engineering." @default.
• W4224018082 created "2022-04-19" @default.
• W4224018082 creator A5000263786 @default.
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• W4224018082 date "2022-01-01" @default.
• W4224018082 modified "2023-10-14" @default.
• W4224018082 title "Swarm Modeling With Dynamic Mode Decomposition" @default.
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• W4224018082 doi "https://doi.org/10.1109/access.2022.3179414" @default.
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