On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching 事件

On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching arXiv:2606.02179v1 Announce Type: cross Abstract: Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as changing loads or boundary conditions, yet the source of this variability remains unclear. We hypothesize that OOD performance is governed by how much information the conditioning signal preserves about