58 citations · 114 across the 4 of their papers we have counts for
3 papers · 1 filter
Learning Stable Galerkin Models of Turbulence with Differentiable Programming
Arvind T. Mohan, Kaushik Nagarajan, Daniel Livescu
Turbulent flow control has numerous applications and building reduced-order models (ROMs) of the flow and the associated feedback control laws is extremely challenging. Despite the…
Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence
Arvind Mohan, Don Daniel, Michael Chertkov +1
High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among…
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
Ryan King, Oliver Hennigh, Arvind Mohan +1
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network…