58 citations · 114 across the 4 of their papers we have counts for
4 papers · 1 filter
Physics-Constrained Generative Adversarial Networks for 3D Turbulence
Dima Tretiak, Arvind T. Mohan, Daniel Livescu
Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images. ML is being applied m…
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…
A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks
Arvind T. Mohan, Datta V. Gaitonde
Reduced Order Modeling (ROM) for engineering applications has been a major research focus in the past few decades due to the unprecedented physical insight into turbulence offered…