2 citations · 5 across the 29 of their papers we have counts for
11 papers · 1 filter
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…
Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances
Ibon Gracia, Dimitris Boskos, Luca Laurenti +1
In this paper, we introduce a data-driven framework for synthesis of provably-correct controllers for general nonlinear switched systems under complex specifications. The focus is…
Data-Driven Permissible Safe Control with Barrier Certificates
Rayan Mazouz, John Skovbekk, Frederik Baymler Mathiesen +3
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is…
Piecewise Stochastic Barrier Functions
Rayan Mazouz, Frederik Baymler Mathiesen, Luca Laurenti +1
This paper presents a novel stochastic barrier function (SBF) framework for safety analysis of stochastic systems based on piecewise (PW) functions. We first outline a general form…
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
Eduardo Figueiredo, Andrea Patane, Morteza Lahijanian +1
Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work we focus on discrete-…
IntervalMDP.jl: Accelerated Value Iteration for Interval Markov Decision Processes
Frederik Baymler Mathiesen, Morteza Lahijanian, Luca Laurenti
In this paper, we present IntervalMDP.jl, a Julia package for probabilistic analysis of interval Markov Decision Processes (IMDPs). IntervalMDP.jl facilitates the synthesis of opti…