arXiv AI October 9, 2026 By Anna Zimmel, Fleur Hendriks, Markus Holzleitner, Florian Sestak, Martin Weichselbaumer, Vlado Menkovski, Johannes Brandstetter

Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems

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Bi-FORK is a generative framework designed to learn one-to-many solution maps in high-dimensional systems undergoing symmetry-breaking bifurcations. It generates complete trajectories via latent flow matching, maintains space and time coherence, and employs repulsion-guided sampling to recover distinct solution branches in a single amortized pass. The method is evaluated on buckling beams, mechanical metamaterials, and Allen‑Cahn phase separation, handling discretizations up to 260,000 points and recovering multimodal solution structures while scaling beyond prior approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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