LGO: A LOCAL GEOMETRY BRANCH FOR AUGMENTING NEURAL OPERATORS IN 3D INDOOR AIRFLOW PREDICTION

Predicting indoor airflow across changing diffuser layouts requires models to capture local supply-jet behaviour, governed by nearby flow-driving boundaries, and room-scale recirculation set by the enclosure. Neural operators efficiently model global flow fields but typically lack explicit local boundary information at each query point. We introduce the Local–Global Operator (LGO), a lightweight local-geometry module using query-relative boundary coordinates stratified by boundary type to preserve small but important supply and return patches. A zeroinitialised projection enables controlled comparison with the original operator. We evaluate LGO on quality-controlled CFD simulations of a single room with diffuser layout as the only varying design parameter. Generalisation is tested using separation-controlled train–test splits based on a dimensionless layout distance, with the threshold set where a nearest-layout retrieval baseline loses predictive skill. Across three neural-operator architectures, LGO adds about 1% more parameters, improves full-field velocity R2 on both splits, and remains more stable as test layouts become more separated from training. Improvements concentrate near supply jets, while no consistent gain is observed in the ASHRAE Standard 55 occupied zone, where model differences are comparable to CFD repeatability and a simple lookup baseline. These results show that query-relative boundary information improves geometric transfer where local boundary forcing is important, at the cost of additional inference time.