Publications

Falsification using Reachability of Surrogate Koopman Models
Black-box falsification problems are most often solved by numerical optimization algorithms. In this work, we propose an alternative …
Stanley Bak, Sergiy Bogomolov, Abdelrahman Hekal, Niklas Kochdumper, Ethan Lew, Andrew Mata, Amir Rahmati
Code DOI
Falsification using Reachability of Surrogate Koopman Models
Learning nonparametric ordinary differential equations from noisy data
Learning nonparametric systems of Ordinary Differential Equations (ODEs) from noisy data is an emerging machine learning topic. We use …
Kamel Lahouel, Michael Wells, Victor Rielly, Ethan Lew, David Lovitz, Bruno M Jedynak
DOI
Learning nonparametric ordinary differential equations from noisy data
AutoKoopman: A Toolbox for Automated System Identification via Koopman Operator Linearization
While Koopman operator linearization has brought many advances for prediction, control, and verification of dynamical systems, its main …
Ethan Lew, Abdelrahman Hekal, Kostiantyn Potomkin, Niklas Kochdumper, Brandon Hencey, Stanley Bak, Sergiy Bogomolov
Code DOI
AutoKoopman: A Toolbox for Automated System Identification via Koopman Operator Linearization
Leveraging Manifold Learning and Relationship Equity Management for Symbiotic Explainable Artificial Intelligence
Improvements in neural methods have led to the unprecedented adoption of AI in domains previously limited to human experts. As these …
Sourya Dey, Adam Karvonen, Ethan Lew, Donya Quick, Panchapakesan Shyamshankar, Ted Hille, Matt Lebeau, Eric Davis
DOI
Leveraging Manifold Learning and Relationship Equity Management for Symbiotic Explainable Artificial Intelligence
Reachability of Koopman Linearized Systems Using Random Fourier Feature Observables and Polynomial Zonotope Refinement
Koopman operator linearization approximates nonlinear systems of differential equations with higher-dimensional linear systems. For …
Stanley Bak, Sergiy Bogomolov, Brandon Hencey, Niklas Kochdumper, Ethan Lew, Kostiantyn Potomkin
DOI
Reachability of Koopman Linearized Systems Using Random Fourier Feature Observables and Polynomial Zonotope Refinement