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Found 6 papers, 2 papers with code
Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures
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.
TTT: A Temporal Refinement Heuristic for Tenuously Tractable Discrete Time Reachability Problems
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.
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems
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.
Verifying Inverse Model Neural Networks
When the forward model that produced the observations is nonlinear and stochastic, solving the inverse problem is very challenging.
OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems
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.
Machine Learning for Generalizable Prediction of Flood Susceptibility
In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network.
