SOURCE-LINKED INTELLIGENCE

Conservative Hybrid Graph Networks for Process Systems with Learned Routing

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Industrial process networks do not maintain a single effective topology while operating: streams are throttled or bypassed, and units move between idle, transition, and active regimes. Models of such systems are typically trained on measured state trajectories while the operating mechanisms that generated them remain latent, and an unconstrained graph network can fit such a trajectory without assigning stable physical meaning to the recovered routing. We address both problems with the Conservative Hybrid Graph Network (CHGN), which learns routing, regime assignment, and removal rates as data-d

Read original source ↗ Open in workspace

recordType

paper

region

Global