58 citations · 106 across the 2 of their papers we have counts for
3 papers
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu +1
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of PDEs is enforcing…
Time-series learning of latent-space dynamics for reduced-order model closure
Romit Maulik, Arvind Mohan, Bethany Lusch +3
We study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equatio…
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…