Search Results for author:
Found 12 papers, 3 papers with code
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures.
Separation of Chaotic Signals by Reservoir Computing
Indeed, our method works well when the component frequency spectra are indistinguishable - a case where a Wiener filter performs essentially no separation.
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.
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system.
Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
For the case of the KS equation, we note that as the system's spatial size is increased, the number of Lyapunov exponents increases, thus yielding a challenging test of our method, which we find the method successfully passes.
Chaotic Dynamics
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
Our technique leverages the results of a machine learning process for short time prediction to achieve our goal.
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.
Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network.
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts.
Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing
Machine learning (ML) has found widespread application over a broad range of important tasks.
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems
We focus on the particularly challenging situation where the past dynamical state time series that is available for ML training predominantly lies in a restricted region of the state space, while the behavior to be predicted evolves on a larger state space set not fully observed by the ML model during training.
