Search Results for author:
Found 7 papers, 3 papers with code
A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics
While we are not aware of a generally accepted best reported mean forecast time for different models in the literature, we report over a factor of 2 increase in the mean forecast time compared to the best performing RC model of Vlachas et. al (2020) for the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics, and we are able to accomplish this using a smaller reservoir size.
Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks
Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems.
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing
We proceed from a scalar time series $s(t_n); t_n = t_0 + n \Delta t$ and using methods of nonlinear time series analysis show how to produce a $D_E > 1$ dimensional time delay embedding space in which the time series has no false neighbors as does the observed $s(t_n)$ time series.
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
In this paper, we develop a systematic formulation based on Monte Carlo sampling to achieve such information transfer.
Machine Learning Classification Informed by a Functional Biophysical System
We present a novel machine learning architecture for classification suggested by experiments on olfactory systems.
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization
Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting time series data.
Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation
The results indicate that these techniques can be applied to estimate the state of a system for the repeated initialization of short-term forecasts, even in the absence of a traditional numerical forecast model.
