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Found 17 papers, 6 papers with code
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems
Under these conditions, we show for several chaotic systems that the hybrid RC-NGRC method with a small reservoir ($N \approx 100$) can achieve prediction performance rivaling that of a pure RC with a much larger reservoir ($N \approx 1000$), illustrating that the hybrid approach offers significant gains in computational efficiency over traditional RCs while simultaneously addressing some of the limitations of NGRCs.
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.
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.
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.
Hybrid Backpropagation Parallel Reservoir Networks
In many real-world applications, fully-differentiable RNNs such as LSTMs and GRUs have been widely deployed to solve time series learning tasks.
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.
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.
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.
Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks
In summary, using PD as a model for chronic progressive diseases, we show that TPC leverages high-dimensional longitudinal datasets for subtype identification and early prediction of individual disease subtype.
Applications Dynamical Systems Biological Physics Data Analysis, Statistics and Probability
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.
Generalization of Learning using Reservoir Computing
Thus, as opposed to training the entire high dimensional reservoir state, the reservoir only needs to train on these unique relationships, allowing the reservoir to perform well with very few training examples.
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
Reservoir observers: Model-free inference of unmeasured variables in chaotic systems
A scheme that accomplishes this is called an “observer.” We consider the case in which a model of the system is unavailable or insufficiently accurate, but “training” time series data of the desired state variables are available for a short period of time, and a limited number of other system variables are continually measured.
The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange
Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept.
Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media
There is a large amount of interest in understanding users of social media in order to predict their behavior in this space.
Community structure in social and biological networks
We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
Statistical Mechanics Disordered Systems and Neural Networks
