A new methodology shows stochastic particle models are structurally identifiable from trajectory data but only locally identifiable from density data, with initial conditions critical to the analysis.
Journal of the Royal Society Interface , volume=
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Wasserstein Lagrangian Mechanics formalizes second-order dynamics in Wasserstein space and provides an algorithm to learn them from observed marginals without specifying the Lagrangian, outperforming gradient flows on various dynamics.
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Structural identifiability of partially-observed stochastic processes: from single-particle trajectories to total particle density data
A new methodology shows stochastic particle models are structurally identifiable from trajectory data but only locally identifiable from density data, with initial conditions critical to the analysis.
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A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
Wasserstein Lagrangian Mechanics formalizes second-order dynamics in Wasserstein space and provides an algorithm to learn them from observed marginals without specifying the Lagrangian, outperforming gradient flows on various dynamics.