An Explicit Surrogate for Gaussian Mixture Flow Matching with Wasserstein Gap Bounds
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
We study training-free flow matching between two Gaussian mixture models (GMMs) using explicit velocity fields that transport one mixture into the other over time. Our baseline approach constructs component-wise Gaussian paths with affine velocity fields satisfying the continuity equation, which yields to a closed-form surrogate for the pairwise kinetic transport cost. In contrast to the exact Gaussian Wasserstein cost, which relies on matrix square-root computations, the surrogate admits a simple analytic expression derived from the kinetic energy of the induced flow. We then analyze how closely this surrogate approximates the exact cost. We prove second-order agreement in a local commuting regime and derive an explicit cubic error bound in the local commuting regime. To handle nonlocal regimes, we introduce a path-splitting strategy that localizes the covariance evolution and enables piecewise application of the bound. We finally compare the surrogate with an exact construction based on the Gaussian Wasserstein geodesic and summarize the results in a practical regime map showing when the surrogate is accurate and the exact method is preferable.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 7 of its 17 references that are in the map. First posted on arXiv 30 Mar 2026.
Builds on
7 of 17 references.
Closest ancestors · 2
Same direction, newest first
- 2024Wasserstein Flow Matching: Generative modeling over families of distributions1 Nov
- 2022Flow Matching for Generative Modeling6 Oct
Other references · 5
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2014On the relation between optimal transport and Schr"odinger bridges: A stochastic control viewpointcited by 99
- 2024Light and Optimal Schr"odinger Bridge Matchingcited by 45
- 2024Go With the Flow: Fast Diffusion for Gaussian Mixture Modelscited by 5
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees
- 2026Preconditioned Flow Matching
- 2026Gaussian Flow-Matching Schedules: Implications for Sampling and Training
- 2025Gaussian Mixture Flow Matching Models
- 2026Correcting CondOT: Exact Finite-Step Sampling in Gaussian Flow Matching
- 2026Same Flow, Different Paths: Variance Reduction in Flow Matching
- 2026The Geometry Behind Diffusion and Flow Matching: Gradient Flows and Geodesics in Wasserstein Space
- 2026Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once