no code implementations • 28 Mar 2025 • Samuel I. Akinwande, Chelsea Sidrane, Mykel J. Kochenderfer, Clark Barrett
As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation.
1 code implementation • 19 Jul 2024 • Chelsea Sidrane, Jana Tumova
Here, we introduce an automatic framework for performing temporal refinement and we demonstrate the effectiveness of this technique on computing approximate reachable sets for nonlinear systems with neural network control policies.
no code implementations • 28 Sep 2022 • Nicholas Rober, Sydney M. Katz, Chelsea Sidrane, Esen Yel, Michael Everett, Mykel J. Kochenderfer, Jonathan P. How
As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe.
no code implementations • 4 Feb 2022 • Chelsea Sidrane, Sydney Katz, Anthony Corso, Mykel J. Kochenderfer
When the forward model that produced the observations is nonlinear and stochastic, solving the inverse problem is very challenging.
2 code implementations • 3 Aug 2021 • Chelsea Sidrane, Amir Maleki, Ahmed Irfan, Mykel J. Kochenderfer
In response to this challenge, we present OVERT: a sound algorithm for safety verification of nonlinear discrete-time closed loop dynamical systems with neural network control policies.
no code implementations • 15 Oct 2019 • Chelsea Sidrane, Dylan J Fitzpatrick, Andrew Annex, Diane O'Donoghue, Yarin Gal, Piotr Biliński
In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network.