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Found 5 papers, 1 papers with code
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy.
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data
Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems.
Model-Free Control of Dynamical Systems with Deep Reservoir Computing
We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer.
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
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 when we have access to both time series data of previous system states and an imperfect model of the full system dynamics.
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.
