150 citations · 386 across the 12 of their papers we have counts for
15 papers
Locality Blended Next Generation Reservoir Computing For Attention Accuracy
Daniel J. Gauthier, Andrew Pomerance, Erik Bollt
We extend an advanced variation of a machine learning algorithm, next-generation reservoir Computing (NGRC), to forecast the dynamics of the Ikeda map of a chaotic laser. The machi…
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…
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
Alexander Wikner, Joseph Harvey, Michelle Girvan +4
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state…
Mathematical Model of Strong Physically Unclonable Functions Based on Hybrid Boolean Networks
Noeloikeau Charlot, Daniel J. Gauthier, Daniel Canaday +1
We introduce a mathematical framework for simulating Hybrid Boolean Network (HBN) Physically Unclonable Functions (PUFs, HBN-PUFs). We verify that the model is able to reproduce th…
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…
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…
The Stability of Boolean Networks with Generalized Canalizing Rules
Andrew Pomerance, Michelle Girvan, Ed Ott
Boolean networks are discrete dynamical systems in which the state (zero or one) of each node is updated at each time t to a state determined by the states at time t-1 of those nod…
The effect of network topology on the stability of discrete state models of genetic control
Andrew Pomerance, Edward Ott, Michelle Girvan +1
Boolean networks have been proposed as potentially useful models for genetic control. An important aspect of these networks is the stability of their dynamics in response to small…
Approximating the Largest Eigenvalue of the Modified Adjacency Matrix of Networks with Heterogeneous Node Biases
Edward Ott, Andrew Pomerance
Motivated by its relevance to various types of dynamical behavior of network systems, the maximum eigenvalue of the adjacency matrix of a network has been considered, and…
Feedback control of unstable cellular solidification fronts
A. J. Pons, A. Karma, S. Akamatsu +4
We present a numerical and experimental study of feedback control of unstable cellular patterns in directional solidification (DS). The sample, a dilute binary alloy, solidifies in…