2 papers
cs.LG2025
A Surrogate-Augmented Symbolic CFD-Driven Training Framework for Accelerating Multi-objective Physical Model Development
Yuan Fang, Fabian Waschkowski, Maximilian Reissmann +3
Computational Fluid Dynamics (CFD)-driven training combines machine learning (ML) with CFD solvers to develop physically consistent closure models with improved predictive accuracy…
physics.flu-dyn2024
A novel data-driven method for augmenting turbulence modeling for unsteady cavitating flows
Dhruv Apte, Nassim Razaaly, Yuan Fang +3
Cavitation is a highly turbulent, multi-phase flow phenomenon that manifests in the form of vapor cavities as a result of a sudden drop in the liquid pressure. The phenomenon has b…