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Learn about Branched Schrödinger Bridge Matching (BranchSBM), a novel framework that extends traditional flow matching and Schrödinger Bridge Matching to handle branched or divergent evolution from a single origin to multiple distinct outcomes. Discover how this method overcomes the limitations of existing approaches that are restricted to unimodal transitions by parameterizing multiple time-dependent velocity fields and growth processes. Explore the mathematical foundations of branched Schrödinger bridges and understand how BranchSBM enables the representation of population-level divergence into multiple terminal distributions. Examine practical applications including multi-path surface navigation, modeling cell fate bifurcations from homogeneous progenitor states, and simulating diverging cellular responses to perturbations. Gain insights into how this framework addresses central problems in generative modeling by predicting intermediate trajectories between initial and target distributions in scenarios involving branched evolution patterns.
