This paper reviews the current GeoAI and machine learning applications in hydrological and
hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial …

We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …

Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-
temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an …

We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …

Lagrangian turbulence lies at the core of numerous applied and fundamental problems
related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere …

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple
spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting …

In recent years the fluid mechanics community has been intensely focused on pursuing
solutions to its long-standing open problems by exploiting the new machine learning (ML) …

Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …

Fast and accurate predictions of turbulent flows are of great importance in the science and
engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural …