A new fluid flow approximation method using a vision transformer and a U-shaped convolutional neural network

Numerical simulation of fluids is important in modeling a variety of physical phenomena, such as weather, climate, aerodynamics, and plasma physics. The Navier–Stokes equations are commonly used to describe fluids, but solving them at a large scale can be computationally expensive, particularly when it comes to resolving small spatiotemporal features. This trade-off between accuracy and tractability can be challenging. In this paper, we propose a novel artificial intelligence-based method for improving fluid flow approximations in computational fluid dynamics (CFD) using deep learning (DL). Our method, called CFDformer, is a surrogate model that can handle both local and global features of CFD input data. It is also able to adjust boundary conditions and incorporate additional flow conditions, such as velocity and pressure. Importantly, CFDformer performs well under different velocities and pressures outside of the flows it was trained on. Through comprehensive experiments and comparisons, we demonstrate that CFDformer outperforms other baseline DL models, including U-shaped convolutional neural network (U-Net) and TransUNet models.