Simulation-Free Learning of Population Dynamics with Wasserstein Lagrangian Residuals
cs.LG, stat.ME, stat.ML
Submitted: 2026-10-02
Updated: 2026-10-02
Code: https://github.com/BasisResearch/stitching
Sources
- Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
- Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schr\"odinger Bridge's End
- DICE: Discrete inverse continuity equation for learning population dynamics
- Likelihood Training of Schr\"odinger Bridge using Forward-Backward SDEs Theory
- Deep Momentum Multi-Marginal Schr\"odinger Bridge
- A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
- Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
- Metric Flow Matching for Smooth Interpolations on the Data Manifold
- Neural Lagrangian Schr\"odinger Bridge: Diffusion Modeling for Population Dynamics
- Characterizing possible failure modes in physics-informed neural networks
- Flow Matching for Generative Modeling
- Generative Path-Finding Method for Wasserstein Gradient Flow
- Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme
- Curly Flow Matching for Learning Non-gradient Field Dynamics
- Learning diffusion at lightspeed
- Improving and generalizing flow-based generative models with minibatch optimal transport
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