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20232026
most citedForward Invariance in Neural Network Controlled Systems

5 citations · 15 across the 17 of their papers we have counts for

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6 papers · 1 filter

eess.SY2023

Interval Signal Temporal Logic from Natural Inclusion Functions

Luke Baird, Akash Harapanahalli, Samuel Coogan

We propose an interval extension of Signal Temporal Logic (STL) called Interval Signal Temporal Logic (\ISTL). Given an STL formula, we consider an interval inclusion function for…

eess.SY2023★ 5 cited

Forward Invariance in Neural Network Controlled Systems

Akash Harapanahalli, Saber Jafarpour, Samuel Coogan

We present a framework based on interval analysis and monotone systems theory to certify and search for forward invariant sets in nonlinear systems with neural network controllers.…

eess.SY2023

Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loops

Saber Jafarpour, Akash Harapanahalli, Samuel Coogan

In this paper, we propose a computationally efficient framework for interval reachability of systems with neural network controllers. Our approach leverages inclusion functions for…

eess.SY2023★ 2 cited

A Toolbox for Fast Interval Arithmetic in numpy with an Application to Formal Verification of Neural Network Controlled Systems

Akash Harapanahalli, Saber Jafarpour, Samuel Coogan

In this paper, we present a toolbox for interval analysis in numpy, with an application to formal verification of neural network controlled systems. Using the notion of natural inc…

eess.SY2023★ 5 cited

Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systems

Akash Harapanahalli, Saber Jafarpour, Samuel Coogan

In this paper, we present a contraction-guided adaptive partitioning algorithm for improving interval-valued robust reachable set estimates in a nonlinear feedback loop with a neur…

eess.SY2023★ 1 cited

Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllers

Saber Jafarpour, Akash Harapanahalli, Samuel Coogan

This paper proposes a computationally efficient framework, based on interval analysis, for rigorous verification of nonlinear continuous-time dynamical systems with neural network…