activity
20142026
most citedFormal Synthesis of Lyapunov Neural Networks

129 citations · 153 across the 17 of their papers we have counts for

collaborators

19 papers

cs.AI2026

Runtime Monitoring of Supermartingale Certificates

Mirco Giacobbe, Thomas A. Henzinger, Konstantin Kueffner +2

Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathematical m…

cs.LG2026

Value Functions as Supermartingale Certificates

Alessandro Abate, Daniel Contro, Mirco Giacobbe +2

Certification methods for stochastic systems provide sufficient proof rules, based on real-valued supermartingale certificates, to determine the almost-sure satisfaction of -reg…

cs.LO2026

Complete -Regular Supermartingale Certificates

Alessandro Abate, Mirco Giacobbe, Sergey Ichtchenko +1

We introduce a general methodology for the construction of sound and complete proof rules for the almost-sure and quantitative acceptance of reactivity properties on time-homogeneo…

cs.CR2026

Zero-Knowledge Model Checking

Pascal Berrang, Mirco Giacobbe, Jacob Swales +1

We introduce a technology to formally verify that a software system satisfies a temporal specification of functional correctness, without revealing the system itself. Our method co…

eess.SY2025

Existence and Synthesis of Multi-Resolution Approximate Bisimulations for Continuous-State Dynamical Systems

Rudi Coppola, Yannik Schnitzer, Mirco Giacobbe +2

We present a fully automatic framework for synthesising compact, finite-state deterministic abstractions of deterministic, continuous-state autonomous systems under locally specifi…

cs.LO2025

Branching Bisimulation Learning

Alessandro Abate, Mirco Giacobbe, Christian Micheletti +1

We introduce a bisimulation learning algorithm for non-deterministic transition systems. We generalise bisimulation learning to systems with bounded branching and extend its applic…

cs.LO2025

Quantitative Supermartingale Certificates

Alessandro Abate, Mirco Giacobbe, Diptarko Roy

We introduce a general methodology for quantitative model checking and control synthesis with supermartingale certificates. We show that every specification that is invariant to ti…

eess.SY2024★ 4 cited

Neural Continuous-Time Supermartingale Certificates

Grigory Neustroev, Mirco Giacobbe, Anna Lukina

We introduce for the first time a neural-certificate framework for continuous-time stochastic dynamical systems. Autonomous learning systems in the physical world demand continuous…

cs.LO2024

Neural Model Checking

Mirco Giacobbe, Daniel Kroening, Abhinandan Pal +1

We introduce a machine learning approach to model checking temporal logic, with application to formal hardware verification. Model checking answers the question of whether every ex…

cs.LO2024

Stochastic Omega-Regular Verification and Control with Supermartingales

Alessandro Abate, Mirco Giacobbe, Diptarko Roy

We present for the first time a supermartingale certificate for -regular specifications. We leverage the Robbins & Siegmund convergence theorem to characterize supermartingale c…

cs.LO2024

Bisimulation Learning

Alessandro Abate, Mirco Giacobbe, Yannik Schnitzer

We introduce a data-driven approach to computing finite bisimulations for state transition systems with very large, possibly infinite state space. Our novel technique computes stut…

cs.LO2023

On the Trade-off Between Efficiency and Precision of Neural Abstraction

Alec Edwards, Mirco Giacobbe, Alessandro Abate

Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models. They comprise a neural ODE and a certified upper bound on the err…

cs.LO2023

Neural Abstractions

Alessandro Abate, Alec Edwards, Mirco Giacobbe

We present a novel method for the safety verification of nonlinear dynamical models that uses neural networks to represent abstractions of their dynamics. Neural networks have exte…

cs.LO2023

Quantitative Verification with Neural Networks

Alessandro Abate, Alec Edwards, Mirco Giacobbe +2

We present a data-driven approach to the quantitative verification of probabilistic programs and stochastic dynamical models. Our approach leverages neural networks to compute tigh…

cs.LG2021★ 16 cited

Neural Termination Analysis

Mirco Giacobbe, Daniel Kroening, Julian Parsert

We introduce a novel approach to the automated termination analysis of computer programs: we use neural networks to represent ranking functions. Ranking functions map program state…

cs.AI2021

Shielding Atari Games with Bounded Prescience

Mirco Giacobbe, Mohammadhosein Hasanbeig, Daniel Kroening +1

Deep reinforcement learning (DRL) is applied in safety-critical domains such as robotics and autonomous driving. It achieves superhuman abilities in many tasks, however whether DRL…

eess.SY2020★ 4 cited

Temporal Logic Trees for Model Checking and Control Synthesis of Uncertain Discrete-time Systems

Yulong Gao, Alessandro Abate, Frank J. Jiang +3

We propose algorithms for performing model checking and control synthesis for discrete-time uncertain systems under linear temporal logic (LTL) specifications. We construct tempora…

eess.SY2020★ 129 cited

Formal Synthesis of Lyapunov Neural Networks

Alessandro Abate, Daniele Ahmed, Mirco Giacobbe +1

We propose an automatic and formally sound method for synthesising Lyapunov functions for the asymptotic stability of autonomous non-linear systems. Traditional methods are either…

cs.CE2014

Model Checking Gene Regulatory Networks

Mirco Giacobbe, Calin C. Guet, Ashutosh Gupta +3

The behaviour of gene regulatory networks (GRNs) is typically analysed using simulation-based statistical testing-like methods. In this paper, we demonstrate that we can replace th…