Machine Learning Tools for Extreme Events Detection/Prediction in Turbulence
Extreme events appear in many fluids mechanical systems, such as in atmospheric flows, oceanography, or wind turbines. These extreme events are sudden, unsteady, transient large nonlinear deviation of the flow away from its mean state. All these events are generally accompanied by detrimental and potentially catastrophic consequences. Therefore, the ability to predict such events is of the utmost importance. However, such a task is extremely challenging because of the underlying complex chaotic dynamics, the high dimensionality of flows and the relatively rare occurrence of extreme events in any dataset.
In this talk, we will present our recent developments in machine learning techniques to support the prediction of such extreme events. Specifically, we will tackle three different aspects. First, we will present a combined dimensionality reduction/clustering approach to identify pathway to extreme events in chaotic systems. Second, we will discuss a reduced-order modelling approach, based on convolutional autoencoder and echo state network, that can learn the dynamics of flow with extreme events. Finally, some aspects related to the possibility of using machine learning-based control will be discussed.
Join Zoom Meeting
https://cwi-nl-zoom.zoom.us/j/89749401482?pwd=DnlkWKPmJTIYb8UYDFd105iNa6wBXD.1
Meeting ID: 897 4940 1482 Passcode: 274138
