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Identifying regions of importance in wall-bounded turbulence through explainable deep learning
One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow.
Aim in Climate Change and City Pollution
The sustainability of urban environments is an increasingly relevant problem.
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow-field data useful for flow analysis, reduced-order modeling, and flow control.
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