Predicting Turbulent Boundary Layer Flows Using Transformers Coupled to the Multi-Physics Simulation Tool m-AIA

Time-marching of turbulent flow fields is computationally expensive with traditional numerical solvers. In this regard, transformer neural network, which has been largely successful in many other technical and scientific domains, can potentially predict complex flow fields faster compared to physics-based solvers. In this study, a transformer model is trained for a turbulent boundary layer problem, which is then coupled to the multi-physics solver m-AIA to make predictions of velocity fields. The method can potentially contribute to significant reduction in computational effort while maintaining high accuracy.