Stratified Turbulence

Stratified turbulence research focuses on understanding and modeling the complex fluid flows characterized by density variations, a key feature in many geophysical and astrophysical systems. Current research heavily utilizes machine learning, employing architectures like convolutional neural networks, recurrent neural networks, and generative adversarial networks (GANs) to improve turbulence simulations, super-resolution techniques, and data-driven closure models. These advancements aim to overcome limitations of traditional numerical methods, particularly in high-Reynolds number flows, leading to more accurate and efficient predictions of turbulent phenomena with applications ranging from weather forecasting to aerospace engineering.