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This work proposes a learning procedure for system identification in interacting particle systems from single-snapshot observations of collective behaviors, unlike existing approaches that rely on observations of trajectories, and test it on a variety of representative models with steady-state and quasi-stationary patt...
This work extends existing variational learning approaches to collective systems with both interaction kernels and environmental/intra-agent forces and introduces a model-selection procedure based on the nonparametric learning framework to identify models that optimally explain a given set of trajectory observations.
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