1 citations · 1 across the 3 of their papers we have counts for
Showing physics.flu-dynShow all
3 papers · 1 filter
physics.flu-dyn2026
Reliable and efficient steady CFD from surrogate predictions through Newton-Krylov correction
Mingcheng Lei, Weishao Tang, Yufei Zhang +1
Neural surrogates offer a promising route to accelerating computationally expensive simulations governed by partial differential equations across science and industry. Their practi…
physics.flu-dyn2026
Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
Yunjia Yang, Jiazhe Li, Yufei Zhang +1
Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehicle acceleration. However, determining…
physics.flu-dyn2024
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning
Yunjia Yang, Runze Li, Yufei Zhang +2
Machine learning-based models provide a promising way to rapidly acquire transonic swept wing flow fields but suffer from large computational costs in establishing training dataset…