633 citations · 653 across the 3 of their papers we have counts for
4 papers
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath +3
Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields…
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
Alexander Wikner, Jaideep Pathak, Brian Hunt +5
We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system…
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…
Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
Jaideep Pathak, Zhixin Lu, Brian R. Hunt +2
We use recent advances in the machine learning area known as 'reservoir computing' to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic…