973 citations · 8k across the 125 of their papers we have counts for
126 papers
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
Alexander Wikner, Joseph Harvey, Michelle Girvan +4
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state…
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems
Dhruvit Patel, Edward Ott
In this paper we consider the machine learning (ML) task of predicting tipping point transitions and long-term post-tipping-point behavior associated with the time evolution of an…
Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing
Shukai Ma, Thomas M. Antonsen, Steven M. Anlage +1
Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures w…
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…
Deep learning estimation of complex reverberant wave fields by a programmable metasurface
Benjamin W. Frazier, Thomas M. Antonsen, Steven M. Anlage +1
Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing…
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…