At the core of uncovering extreme events such as floods is the physics of fluids – specifically turbulent flows. Researchers leveraged a computer-vision deep learning technique and adapted it for nonlinear analysis of extreme events in wall-bounded turbulent flows, which are pervasive in numerous physics and engineering applications and impact wind and hydrokinetic energy, among others. Results show the technique employed can be invaluable for accurately identifying the sources of extreme events in a completely data-driven manner.
Identifying the underlying cause of extreme events such as floods, heavy downpours or tornados is immensely difficult and can take a concerted effort by scientists over several decades to arrive at feasible physical explanations.
Researchers from Florida Atlantic University's College of Engineering and Computer Science leveraged a computer-vision deep learning technique and adapted it for nonlinear analysis of extreme events in wall-bounded turbulent flows, which are pervasive in numerous physics and engineering applications and impact wind and hydrokinetic energy, among others.
The general framework the researchers employed leverages a combination of 3D CNNs and the newly modified multi-layer GradCAM technique, which provides an explainable interpretation of a CNN's learned associations related to ejection events in wall-bounded turbulent flows. Xingquan Zhu, Ph.D., a professor in the Department of Electrical Engineering and Computer Science within FAU's College of Engineering and Computer Science is a co-author of the study.
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