150 citations
4 papers
Tailored Forecasting from Short Time Series via Meta-learning
Declan A. Norton, Edward Ott, Andrew Pomerance +2
Machine learning models can effectively forecast dynamical systems from time-series data, but they typically require large amounts of past data, making forecasting particularly cha…
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…
Stability of Boolean networks: The joint effects of topology and update rules
Shane Squires, Andrew Pomerance, Michelle Girvan +1
We study the stability of orbits in large Boolean networks with given complex topology. We impose no restrictions on the form of the update rules, which may be correlated with loca…