**TandemFoilSet**: Datasets for Flow Field Prediction of Tandem-Airfoil Through the Reuse of Single Airfoils

ICLR 2026 Conference Submission14913 Authors

Keywords: Physics-informed Graph Neural Network; Tandem-Airfoil; Flow Field Prediction; CFD; Aerodynamics;

Abstract: Accurate simulation of flow fields around tandem geometries is critical for engineering design but remains computationally intensive. Existing machine learning approaches typically focus on simpler cases and lack evaluation on multi-body configurations. To support research in this area, we present **TandemFoilSet**: five tandem-airfoil datasets comprising over 4000 fluid simulations, paired with their single-airfoil counterparts. We provide benchmark results of a curriculum learning framework using a directional integrated distance representation, residual pre-training, training schemes based on freestream conditions and smooth-combined estimated fields, and a domain decomposition strategy. Evaluations demonstrate notable gains in prediction accuracy. We believe these datasets will enable future work on scalable, data-driven flow prediction for tandem-airfoil scenarios.

Primary Area: datasets and benchmarks

Submission Number: 14913