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Found 8 papers, 1 papers with code
Controlling Chaotic Maps using Next-Generation Reservoir Computing
In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems.
Learning unseen coexisting attractors
Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system.
Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
Forecasting the behavior of high-dimensional dynamical systems using machine learning requires efficient methods to learn the underlying physical model.
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing
Reservoir computers are powerful tools for chaotic time series prediction.
Next Generation Reservoir Computing
Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data.
Reservoir Computing with Superconducting Electronics
The rapidity and low power consumption of superconducting electronics makes them an ideal substrate for physical reservoir computing, which commandeers the computational power inherent to the evolution of a dynamical system for the purposes of performing machine learning tasks.
Symmetry-Aware Reservoir Computing
For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming hand crafted artificial neural networks.
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization.
