369 citations · 992 across the 36 of their papers we have counts for
37 papers
Convolutional method for data assimilation An improved method on neuronal electrophysiological data
Dawei Li, Henry D. I. Abarbanel
We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. Wh…
Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks
Jason A. Platt, Stephen G. Penny, Timothy A. Smith +2
Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invari…
Data Driven Regional Weather Forecasting: Example using the Shallow Water Equations
Randall Clark, Henry Abarbanel, Luke C. Fairbanks +2
Using data alone, without knowledge of underlying physical models, nonlinear discrete time regional forecasting dynamical rules are constructed employing well tested methods from a…
A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics
Jason A. Platt, Stephen G. Penny, Timothy A. Smith +2
A reservoir computer (RC) is a type of simplified recurrent neural network architecture that has demonstrated success in the prediction of spatiotemporally chaotic dynamical system…
Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation
Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen +4
Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. To achieve this, recurrent neural networks (RNNs) are i…
Robust Forecasting using Predictive Generalized Synchronization in Reservoir Computing
Jason A. Platt, Adrian S. Wong, Randall Clark +2
Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting timeseries data. As with all RNNs, selecting the hyperparameters presents a challenge whe…