Artificial Surrogate Model for Computational Fluid Dynamics

Simulating fluid dynamics is challenging due to the computational complexity of processing high-dimensional data, which often requires significant time.Fluid behavior is typically governed by partial differential equations (PDEs), and the complexity escalates when obstacles disrupt the flow, reinforcing vorticity formation.Vorticity describes the local rotational motion of a fluid.In this paper, we present a data-driven approach to automate PDE simulations and develop surrogate models to generate fluid dynamics based on the Krmn vortex street.Our approach aims to generate accurate fluid simulations with faster computation through architectural adjustments.