Chaotic Flow

Chaotic flow research focuses on understanding and manipulating the unpredictable, highly sensitive dynamics of complex systems, often involving fluid mechanics. Current efforts concentrate on developing data-driven models, such as echo state networks, variational autoencoders, and convolutional neural networks, to predict, control, and analyze these flows, including the identification and suppression of extreme events. These advancements are improving the accuracy of forecasting in areas like fluid dynamics and weather prediction, and enabling more effective control strategies for mitigating potentially damaging events in various applications. The development of efficient reduced-order models is also a key focus, aiming to improve computational efficiency while maintaining accuracy.