Searched for: author%3A%22Laurenti%2C+L.%22
(1 - 13 of 13)
document
Tognan, A. (author), Patanè, Andrea (author), Laurenti, L. (author), Salvati, Enrico (author)
Accurate fatigue assessment of material plagued by defects is of utmost importance to guarantee safety and service continuity in engineering components. This study shows how state-of-the-art semi-empirical models can be endowed with additional defect descriptors to probabilistically predict the occurrence of fatigue failures by exploiting...
journal article 2024
document
Mathiesen, Frederik Baymler (author), Calvert, S.C. (author), Laurenti, L. (author)
Providing non-trivial certificates of safety for non-linear stochastic systems is an important open problem. One promising solution to address this problem is the use of barrier functions. Barrier functions are functions whose composition with the system forms a Martingale and enable the computation of the probability that the system stays...
journal article 2023
document
Gracia, Ibon (author), Boskos, D. (author), Laurenti, L. (author), Mazo, M. (author)
We present a novel framework for formal control of uncertain discrete-time switched stochastic systems against probabilistic reach-avoid specifications. In particular, we consider stochastic systems with additive noise, whose distribution lies in an ambiguity set of distributions that are ε−close to a nominal one according to the Wasserstein...
conference paper 2023
document
Delimpaltadakis, Giannis (author), Lahijanian, Morteza (author), Mazo, M. (author), Laurenti, L. (author)
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with...
conference paper 2023
document
Skovbekk, John (author), Laurenti, L. (author), Frew, Eric (author), Lahijanian, Morteza (author)
Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with nonstandard (e.g., non-affine, non-symmetric, non-unimodal) noise distributions for verification purposes. The method uses a finite...
journal article 2023
document
Adams, S.J.L. (author), Patanè, Andrea (author), Lahijanian, Morteza (author), Laurenti, L. (author)
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input points T ⊂ R<sup>n</sup>, BNN-DP computes lower and upper bounds on the BNN's predictions for all the points in T. The framework is based on an interpretation of BNNs as...
conference paper 2023
document
Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)
Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...
journal article 2023
document
Tognan, A. (author), Laurenti, L. (author), Salvati, E. (author)
Background: Over the past 20 years, the Contour Method (CM) has been extensively implemented to evaluate residual stress at the macro scale, especially in products where material processing is involved. Despite this, insufficient attention has been devoted to addressing the problems of input data filtering and residual stress uncertainties...
journal article 2022
document
Adams, S.J.L. (author), Lahijanian, Morteza (author), Laurenti, L. (author)
Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control systems with complicated physics or black-box components. Due to complexity of NNs, however, existing methods are unable to synthesize complex behaviors with guarantees for NN dynamic models (NNDMs). This letter introduces a control synthesis framework for...
journal article 2022
document
Salvati, Enrico (author), Tognan, Alessandro (author), Laurenti, L. (author), Pelegatti, Marco (author), De Bona, Francesco (author)
Defects in additively manufactured materials are one of the leading sources of uncertainty in mechanical fatigue. Fracture mechanics concepts are useful to evaluate their influence, nevertheless, these approaches cannot account for the real morphology of defects. Preliminary attempts to exploit a more comprehensive description of defects can...
journal article 2022
document
Benussi, Elias (author), Patane, Andrea (author), Wicker, Matthew (author), Laurenti, L. (author), Kwiatkowska, Marta (author)
We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs). In particular, we work with the ϵ-δ-IF formulation, which, given a NN and a similarity metric learnt from data, requires that the output difference between any pair of ϵ-similar individuals is bounded by a maximum decision tolerance δ ≥ 0...
conference paper 2022
document
Delimpaltadakis, Giannis (author), Laurenti, L. (author), Mazo, M. (author)
Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting its sampling patterns. Finite-state abstractions, capturing the sampling behaviour of ETC systems, have proven promising in this respect. So far, such abstractions have...
conference paper 2021
document
Jackson, John (author), Laurenti, L. (author), Frew, Eric (author), Lahijanian, Morteza (author)
We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via...
conference paper 2021
Searched for: author%3A%22Laurenti%2C+L.%22
(1 - 13 of 13)