34 citations · 69 across the 24 of their papers we have counts for
7 papers · 1 filter
A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations
Spencer H. Bryngelson, Alexis Charalampopoulos, Themistoklis P. Sapsis +1
Phase-averaged dilute bubbly flow models require high-order statistical moments of the bubble population. The method of classes, which directly evolve bins of bubbles in the probab…
Bubbles in Turbulent Flows: Data-driven, kinematic models with memory terms
Zhong Yi Wan, Petr Karnakov, Petros Koumoutsakos +1
We present data driven kinematic models for the motion of bubbles in high-Re turbulent fluid flows based on recurrent neural networks with long-short term memory enhancements. The…
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt +4
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (R…
Machine-Learning Ocean Dynamics from Lagrangian Drifter Trajectories
Nikolas O. Aksamit, Themistoklis P. Sapsis, George Haller
Lagrangian ocean drifters provide highly accurate approximations of ocean surface currents but are sparsely located across the globe. As drifters passively follow ocean currents, t…
Learning the Tangent Space of Dynamical Instabilities from Data
Antoine Blanchard, Themistoklis P. Sapsis
For a large class of dynamical systems, the optimally time-dependent (OTD) modes, a set of deformable orthonormal tangent vectors that track directions of instabilities along any t…
Closed-loop adaptive control of extreme events in a turbulent flow
Mohammad Farazmand, Themistoklis P. Sapsis
Extreme events that arise spontaneously in chaotic dynamical systems often have an adverse impact on the system or the surrounding environment. As such, their mitigation is highly…