15.6k citations · 17.7k across the 43 of their papers we have counts for
3 papers · 2 filters
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data
Daniel Canaday, Andrew Pomerance, Michelle Girvan
Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems. Ho…
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Keshav Srinivasan, Nolan Coble, Joy Hamlin +3
Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts. Here we present a machine learning scheme for this task…
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
Alexander Wikner, Jaideep Pathak, Brian R. Hunt +3
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state…