55 citations · 63 across the 5 of their papers we have counts for
5 papers
Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence
Timothy A. Smith, Stephen G. Penny, Jason A. Platt +1
The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit…
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…
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…
`Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form
Tse-Chun Chen, Stephen G. Penny, Timothy A. Smith +1
Next generation reservoir computing based on nonlinear vector autoregression (NVAR) is applied to emulate simple dynamical system models and compared to numerical integration schem…
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…