Bifurcating systems violate the one-to-one assumption in standard neural surrogates; Bi-FORK solves this by treating bifurcations as a generative modeling problem, enabling amortized prediction of all solution branches simultaneously.
Bi-FORK is a generative model that learns one-to-many solution maps in high-dimensional physical systems undergoing bifurcations—where a single input produces multiple equally valid outputs. Using latent flow matching and repulsion-guided sampling, it generates complete trajectories while preserving spatial and temporal coherence, scaling to systems with hundreds of thousands of dimensions.