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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3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Unbalanced Schrödinger Bridge (USB) provides a tractable, simulation-free solution to the Branching Schrödinger Bridge problem for modeling discrete birth-death dynamics at single-cell resolution from snapshot data.
PiFM extends Flow Matching to multi-parameter settings by enforcing path-independent flows that approximate Wasserstein barycenters under suitable assumptions.
citing papers explorer
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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.
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Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
Unbalanced Schrödinger Bridge (USB) provides a tractable, simulation-free solution to the Branching Schrödinger Bridge problem for modeling discrete birth-death dynamics at single-cell resolution from snapshot data.
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Path-independent Flow Matching for Multi-parameter Generative Dynamics
PiFM extends Flow Matching to multi-parameter settings by enforcing path-independent flows that approximate Wasserstein barycenters under suitable assumptions.