3 citations · 3 across the 14 of their papers we have counts for
7 papers · 1 filter
From Formal Methods to Data-Driven Safety Certificates of Unknown Large-Scale Networks
Omid Akbarzadeh, Behrad Samari, Amy Nejati +1
In this work, we propose a data-driven scheme within a compositional framework with noisy data to design robust safety controllers in a fully decentralized fashion for large-scale…
Data-Efficient Control of Polynomial Systems via Physics-Guided Quadratic Constraints
MohammadHossein Ashoori, Ali Aminzadeh, Amy Nejati +1
This work addresses the critical challenge of guaranteeing safety for complex dynamical systems where precise mathematical models are uncertain and data measurements are corrupted…
Data-Driven Safety Certificates of Infinite Networks with Unknown Models and Interconnection Topologies
Mahdieh Zaker, Amy Nejati, Abolfazl Lavaei
Infinite networks are complex interconnected systems comprising a countably infinite number of subsystems, for which no fixed upper bound on the number of participating subsystems…
ARCH-COMP25 Category Report: Stochastic Models
Alessandro Abate, Omid Akbarzadeh, Henk A. P. Blom +14
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks a…
TRUST: Stability and Safety Controller Synthesis for Unknown Dynamical Models Using a Single Trajectory
Jamie Gardner, Ben Wooding, Amy Nejati +1
TRUST is an open-source software tool developed for data-driven controller synthesis of dynamical systems with unknown mathematical models, ensuring either stability or safety prop…
From Data to Global Asymptotic Stability of Unknown Large-Scale Networks with Provable Guarantees
Mahdieh Zaker, Amy Nejati, Abolfazl Lavaei
We offer a compositional data-driven scheme for synthesizing controllers that ensure global asymptotic stability (GAS) across large-scale interconnected networks, characterized by…
Model Order Reduction from Data with Certification
Behrad Samari, Amy Nejati, Abolfazl Lavaei
Model order reduction (MOR) involves offering low-dimensional models that effectively approximate the behavior of complex high-order systems. Due to potential model complexities an…