Ruijie Li

Neural surrogates for three-dimensional fluid simulation need large sets of costly high-fidelity CFD labels. SimpFlow instead generates supervision from simplified physics: a source-panel potential-flow solver that needs only a surface mesh labels about 9,000 unlabeled ShapeNet geometries with velocity and pressure fields, reusing precomputed velocity fields across inflow directions for an amortized 0.111 s per example. A Transolver pretrained on these labels and fine-tuned on independent CFD datasets improves over training from scratch in all 24 task–budget settings across five flow tasks; with fine-tuning fixed at 200 epochs, interpolation indicates 35–68% fewer CFD training cases are needed to match scratch error at each task's largest budget.

Neural surrogates for three-dimensional fluid simulation need large sets of costly high-fidelity CFD labels. SimpFlow instead generates supervision from simplified physics: a source-panel potential-flow solver that needs only a surface mesh labels about 9,000 unlabeled ShapeNet geometries with velocity and pressure fields, reusing precomputed velocity fields across inflow directions for an amortized 0.111 s per example. A Transolver pretrained on these labels and fine-tuned on independent CFD datasets improves over training from scratch in all 24 task–budget settings across five flow tasks; with fine-tuning fixed at 200 epochs, interpolation indicates 35–68% fewer CFD training cases are needed to match scratch error at each task's largest budget.