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Marco de Angelis
Marco de Angelis
Lecturer in Engineering, University of Strathclyde
Verified email at strath.ac.uk - Homepage
Title
Cited by
Cited by
Year
Advanced line sampling for efficient robust reliability analysis
M de Angelis, E Patelli, M Beer
Structural safety 52, 170-182, 2015
2222015
Reliability-based optimal design of nonlinear viscous dampers for the seismic protection of structural systems
D Altieri, E Tubaldi, M De Angelis, E Patelli, A Dall’Asta
Bulletin of Earthquake Engineering 16, 963-982, 2018
772018
OpenCossan: An efficient open tool for dealing with epistemic and aleatory uncertainties
E Patelli, M Broggi, M Angelis, M Beer
Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and …, 2014
742014
Uncertainty management in multidisciplinary design of critical safety systems
E Patelli, DA Alvarez, M Broggi, M de Angelis
Journal of Aerospace Information Systems 12 (1), 140-169, 2015
712015
OpenCossan 2.0: an efficient computational toolbox for risk, reliability and resilience analysis
E Patelli, S Tolo, H George-Williams, J Sadeghi, R Rocchetta, ...
392018
From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information
A Gray, A Wimbush, M de Angelis, PO Hristov, D Calleja, E Miralles-Dolz, ...
Mechanical Systems and Signal Processing 165, 108210, 2022
362022
"No test is better than a bad test": Impact of diagnostic uncertainty in mass testing on the spread of Covid-19
N Gray, D Calleja, A Wimbush, E Miralles-Dolz, A Gray, M De Angelis, ...
medRxiv, 2020
352020
Robust propagation of probability boxes by interval predictor models
J Sadeghi, M de Angelis, E Patelli
Structural Safety 82, 101889, 2020
282020
On the Robust Estimation of Small Failure Probabilities for Strong Nonlinear Models
M Faes, J Sadeghi, M Broggi, M De Angelis, E Patelli, M Beer, D Moens
ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 5 (4), 2019
252019
Efficient training of interval Neural Networks for imprecise training data
J Sadeghi, M De Angelis, E Patelli
Neural Networks 118, 338-351, 2019
202019
An integrated and efficient numerical framework for uncertainty quantification: application to the nasa langley multidisciplinary uncertainty quantification challenge
E Patelli, DA Alvarez, M Broggi, M de Angelis
16th AIAA Non-Deterministic Approaches Conference, 1501, 2014
182014
Development of a digital twin operational platform using Python Flask
MS Bonney, M de Angelis, M Dal Borgo, L Andrade, S Beregi, N Jamia, ...
Data-Centric Engineering 3, 2022
162022
Frequentist history matching with Interval Predictor Models
J Sadeghi, M de Angelis, E Patelli
Applied Mathematical Modelling 61, 29-48, 2018
162018
Low-cost battery monitoring by converter-based electrochemical impedance spectroscopy
R Ferrero, C Wu, A Carboni, S Toscani, M De Angelis, H George-Williams, ...
2017 IEEE International Workshop on Applied Measurements for Power Systems …, 2017
162017
Forced Monte Carlo simulation strategy for the design of maintenance plans with multiple inspections
M de Angelis, E Patelli, M Beer
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A …, 2017
162017
Why the 1-Wasserstein distance is the area between the two marginal CDFs
M De Angelis, A Gray
arXiv preprint arXiv:2111.03570, 2021
112021
Pseudo credal networks for inference with probability intervals
HDE Lugo, S Tolo, M De Angelis, E Patelli
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2019
11*2019
Digital Twin Operational Platform for Connectivity and Accessibility using Flask Python
MS Bonney, M de Angelis, D Wagg, M Dal Borgo
2021 ACM/IEEE International Conference on Model Driven Engineering Languages …, 2021
102021
Line sampling for assessing structural reliability with imprecise failure probabilities
M de Angelis, E Patelli, M Beer
Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and …, 2014
102014
Projecting interval uncertainty through the discrete Fourier transform: An application to time signals with poor precision
M Behrendt, M de Angelis, L Comerford, Y Zhang, M Beer
Mechanical Systems and Signal Processing 172, 108920, 2022
92022
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Articles 1–20