One steady-state snapshot suffices to recover particle interaction kernels, no trajectories, self-reported
Baoli Hao, Mauro Maggioni and Ming Zhong propose identifying interacting particle systems from a single steady-state snapshot of collective behavior, regularizing the ill-posed inverse problem with empirical distributions of configurations under different unobserved initial conditions, with no trajectory data needed.
ImportanceLocalEvidenceE2 unreplicated
A single steady-state snapshot of collective behavior can now identify an interacting particle system, with no trajectory observations at all.
Identifying such systems previously relied on trajectory data; Baoli Hao, Mauro Maggioni and Ming Zhong instead regularize this ill-posed inverse problem using empirical distributions of configurations under different unobserved initial conditions.
The authors self-report stable and accurate recovery of the interaction kernels across multiple representative steady-state and quasi-steady-state models.
Boundary: the results are self-reported by the authors and not yet independently reproduced; the work is an arXiv preprint (arXiv:2609.12004), submitted September 10 and updated to v2 on September 16.