2 citations · 5 across the 29 of their papers we have counts for
33 papers
Safe-by-Design Learning via Energy-based Neural Networks
Simone Betteti, Morteza Lahijanian, Luca Laurenti
Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settings. Safety is commonly establi…
Stochastic Nonlinear Model Predictive Control with Gaussian Mixture Uncertainty Propagation
Konstantinos Prattis, Luca Laurenti, Azita Dabiri
We propose a novel Stochastic Nonlinear Model Predictive Control (SNMPC) framework for nonlinear systems with additive noise. Building on recent advances in nonlinear uncertainty p…
Clustered Randomized Smoothing for Stochastic Prediction Functions
Eduardo Figueiredo, Frederik Mathiesen, Julian Schumann +3
Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions a critical r…
Provably Safe Motion Planning Under Unknown Disturbances
Ibon Gracia, Qi Heng Ho, Luca Laurenti +1
We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or…
Stochastic Barrier Certificates in the Presence of Dynamic Obstacles
Rayan Mazouz, Luca Laurenti, Morteza Lahijanian
In this work, we focus on safety verification of stochastic systems in dynamic environments. We introduce both time-invariant and time-varying barrier certificates for discrete-tim…
Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
Simone Betteti, Luca Laurenti
Energy-based models (EBMs) implement inference as gradient descent on a learned Lyapunov function, yielding interpretable, structure-preserving alternatives to black-box neural ODE…
Suppressed excitonic effects enable high mobility, high-yield photoconductivity in a two-dimensional polymer crystal with axial pyridine coordination
Shuai Fu, Ye Yang, Guoquan Gao +8
Two-dimensional polymers (2DPs) and their layer-stacked covalent organic frameworks (2D COFs) offer modular, atomically precise platforms for organic optoelectronics, yet their pho…
Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction
Julian F. Schumann, Eduardo Figueiredo, Frederik Baymler Mathiesen +3
Accurate and robust trajectory prediction is essential for safe and efficient autonomous driving, yet recent work has shown that even state-of-the-art prediction models are highly…
Efficient Distribution Learning with Error Bounds in Wasserstein Distance
Eduardo Figueiredo, Steven Adams, Luca Laurenti
The Wasserstein distance has emerged as a key metric to quantify distances between probability distributions, with applications in various fields, including machine learning, contr…
StochasticBarrier.jl: A Toolbox for Stochastic Barrier Function Synthesis
Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti +1
We present StochasticBarrier.jl, an open-source Julia-based toolbox for generating Stochastic Barrier Functions (SBFs) for safety verification of discrete-time stochastic systems w…
Verification of Unknown Dynamical Systems via Autoencoder Latent Space
Robert Reed, Luca Laurenti, Morteza Lahijanian
Formal verification provides a powerful framework for proving that dynamical systems satisfy their specifications. However, these techniques face scalability challenges in high-dim…
discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
Steven Adams, Elize Alwash, Luca Laurenti
We present discretize_distributions, a Python package that efficiently constructs discrete approximations of Gaussian mixture distributions and provides guarantees on the approxima…
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings +2
Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as neural networks have shown great…
Piecewise Control Barrier Functions for Stochastic Systems
Rayan Mazouz, Luca Laurenti, Morteza Lahijanian
This paper presents a method for the simultaneous synthesis of a barrier certificate and a safe controller for discrete-time nonlinear stochastic systems. Our approach, based on pi…
Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance
Eduardo Figueiredo, Steven Adams, Peyman Mohajerin Esfahani +1
In this paper, we consider the problem of propagating an uncertain distribution by a possibly non-linear function and quantifying the resulting uncertainty. We measure the uncertai…
Formal Uncertainty Propagation for Stochastic Dynamical Systems with Additive Noise
Steven Adams, Eduardo Figueiredo, Luca Laurenti
In this paper, we consider discrete-time non-linear stochastic dynamical systems with additive process noise in which both the initial state and noise distributions are uncertain.…
Certified Neural Approximations of Nonlinear Dynamics
Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano +2
Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such…
Memory-dependent abstractions of stochastic systems through the lens of transfer operators
Adrien Banse, Giannis Delimpaltadakis, Luca Laurenti +2
With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochast…
Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances
Ibon Gracia, Luca Laurenti, Manuel Mazo +2
In this paper, we present a novel framework to synthesize robust strategies for discrete-time nonlinear systems with random disturbances that are unknown, against temporal logic sp…
Scalable control synthesis for stochastic systems via structural IMDP abstractions
Frederik Baymler Mathiesen, Sofie Haesaert, Luca Laurenti
This paper introduces a novel abstraction-based framework for controller synthesis of nonlinear discrete-time stochastic systems. The focus is on probabilistic reach-avoid specific…
A data-driven approach for safety quantification of non-linear stochastic systems with unknown additive noise distribution
Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert +2
In this paper, we present a novel data-driven approach to quantify safety for non-linear, discrete-time stochastic systems with unknown noise distribution. We define safety as the…
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
Chun-Wei Kong, Luca Laurenti, Jay McMahon +1
Stochastic differential equations are commonly used to describe the evolution of stochastic processes. The state uncertainty of such processes is best represented by the probabilit…
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
Robert Reed, Luca Laurenti, Morteza Lahijanian
Gaussian Process Regression (GPR) is a powerful and elegant method for learning complex functions from noisy data with a wide range of applications, including in safety-critical do…
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Steven Adams, Andrea Patanè, Morteza Lahijanian +1
Infinitely wide or deep neural networks (NNs) with independent and identically distributed (i.i.d.) parameters have been shown to be equivalent to Gaussian processes. Because of th…