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

arXiv:2606.02179v1 Announce Type: new 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 the adjoint sensitivity (reduced gradient) that drives classical TO. Modeling the TO pipeline as a causal Markov chain, the Data Processing Inequality establishes that, under this abstraction, the sensi

Source: arXiv cs.LG — read the full report at the original publisher.

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