Learning Collective Behaviors from Observation
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Abstract (arXiv)
We present a comprehensive examination of learning methodologies employed for the structural identification of dynamical systems. These techniques are designed to elucidate emergent phenomena within intricate systems of interacting agents. Our approach not only ensures theoretical convergence guarantees but also exhibits computational efficiency when handling high-dimensional observational data. The methods adeptly reconstruct both first- and second-order dynamical systems, accommodating observation and stochastic noise, intricate interaction rules, absent interaction features, and real-world observations in agent systems. The foundational aspect of our learning methodologies resides in the formulation of tailored loss functions using the variational inverse problem approach, inherently equipping our methods with dimension reduction capabilities.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 69 references that are in the map. First posted on arXiv 1 Nov 2023.
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24 of 69 references.
References · 24
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12 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.