Data discovery of low dimensional fluid dynamics of turbulent flows (2401.05449v1)

Abstract: Discovering governing equations from data, in particular high dimensional data, is challenging in various fields of science and engineering, and it has potential to revolutionise the science and technology in this big data era. This paper combines sparse identification and deep learning with non-linear fluid dynamics, in particular the turbulent flows, to discover governing equations of nonlinear fluid dynamics in the lower nonlinear manifold space. The autoencoder deep neural network is used to project the high dimensional space into a lower dimensional nonlinear manifold space. The Proper Orthogonal Decomposition (POD) is then used to stabilise the nonlinear manifold space in order to guarantee a stable manifold space for pattern or equations discovery for the highly nonlinear problems such as turbulent flows. Sparse regression is then used to discover the lower dimensional governing equations of fluid dynamics in the lower dimensional nonlinear manifold space. What distinguishes this approach is its ability to discover a lower dimensional governing equations of fluid dynamics in the nonlinear manifold space. We demonstrate this method on a number of high-dimensional fluid dynamic systems such as lock exchange, flow past one and two cylinders. The results demonstrate that the resulting method is capable of discovering lower dimensional governing equations that took researchers in this community many decades years to resolve. In addition, this model discovers dynamics in a lower dimensional manifold space, thus leading to great computational efficiency, model complexity and avoiding overfitting. It also provides a new insight for our understanding of sciences such as turbulent flows.