Vlado Menkovski

Machine learning researcher in the Department of Mathematics & Computer Science at Eindhoven University of Technology, where he works on applying generative and geometric deep learning methods to physical-systems simulation.

He matters to this vault as the one author shared across four papers in its bifurcation-generative-modeling thread: Bi-FORK (Zimmel et al., arXiv:2610.12449, 2026), its predecessor equivariant flow-matching paper (Hendriks, Rokoš, Doškář, Geers & Menkovski, arXiv:2509.03340, 2025), the mechanical-metamaterials dataset paper Bi-FORK evaluates on (Hendriks, Menkovski, Doškář, Geers, Verbeek & Rokoš, Scientific Data 12:1880, 2025), and STFlow (Bennema ten Brinke, Minartz & Menkovski, arXiv:2505.18647, 2026) — which Bi-FORK itself uses as one of only two comparison baselines on its flagship Beam3D benchmark. That last overlap is the specific finding behind claim-bi-fork-stflow-baseline-shares-author-reduces-independence: one of Bi-FORK's two "independent" baselines is not independent of Bi-FORK's own senior authorship. He is, at present, the single most load-bearing name for assessing how independent this paper-cluster's benchmark claims actually are of each other.

References

• claim-bi-fork-whole-trajectory-latent-flow-matching-for-global-branch-consistency
• claim-bi-fork-260k-point-scale-is-first-demonstration-of-one-to-many-bifurcation-recovery
• claim-bi-fork-particle-guidance-sampler-is-parallelized-deflation-analog
• claim-bi-fork-stflow-baseline-shares-author-reduces-independence