150 citations · 151 across the 2 of their papers we have counts for
6 papers
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data
Daniel Canaday, Andrew Pomerance, Michelle Girvan
Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems. Ho…
High-Resolution Waveform Capture Device on a Cyclone-V FPGA
Noeloikeau Charlot, Daniel J. Gauthier, Andrew Pomerance
We introduce the waveform capture device (WCD), a flexible measurement system capable of recording complex digital signals on trillionth-of-a-second (ps) time scales. The WCD is im…
Model-Free Control of Dynamical Systems with Deep Reservoir Computing
Daniel Canaday, Andrew Pomerance, Daniel J Gauthier
We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as…
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
Alexander Wikner, Jaideep Pathak, Brian Hunt +5
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…
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
Aaron Griffith, Andrew Pomerance, Daniel J. Gauthier
We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir pe…
Hybrid Boolean Networks as Physically Unclonable Functions
Noeloikeau Charlot, Daniel Canaday, Andrew Pomerance +1
We introduce a Physically Unclonable Function (PUF) based on an ultra-fast chaotic network known as a Hybrid Boolean Network (HBN) implemented on a field programmable gate array. T…