| Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks C Sbarufatti, M Corbetta, M Giglio, F Cadini Journal of Power Sources 344, 128-140, 2017 | 236 | 2017 |
| Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis RG Nascimento, M Corbetta, CS Kulkarni, FAC Viana Journal of Power Sources 513, 230526, 2021 | 145 | 2021 |
| A Bayesian framework for fatigue life prediction of composite laminates under co-existing matrix cracks and delamination M Corbetta, C Sbarufatti, M Giglio, A Saxena, K Goebel Composite Structures 187, 58-70, 2018 | 62 | 2018 |
| Optimization of nonlinear, non-Gaussian Bayesian filtering for diagnosis and prognosis of monotonic degradation processes M Corbetta, C Sbarufatti, M Giglio, MD Todd Mechanical Systems and Signal Processing 104, 305-322, 2018 | 61 | 2018 |
| Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction C Sbarufatti, M Corbetta, A Manes, M Giglio International Journal of Fatigue 83, 10-23, 2016 | 59 | 2016 |
| Real-time prognosis of crack growth evolution using sequential Monte Carlo methods and statistical model parameters M Corbetta, C Sbarufatti, A Manes, M Giglio IEEE Transactions on Reliability 64 (2), 736-753, 2014 | 58 | 2014 |
| On dynamic state-space models for fatigue-induced structural degradation M Corbetta, C Sbarufatti, A Manes, M Giglio International Journal of Fatigue 61, 202-219, 2014 | 45 | 2014 |
| Real-time uav trajectory prediction for safety monitoring in low-altitude airspace M Corbetta, P Banerjee, W Okolo, G Gorospe, DG Luchinsky Aiaa aviation 2019 forum, 3514, 2019 | 42 | 2019 |
| Architecture and information requirements to assess and predict flight safety risks during highly autonomous urban flight operations S Young, E Ancel, A Moore, E Dill, C Quach, J Foster, K Darafsheh, ... | 40 | 2020 |
| Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis F Cadini, C Sbarufatti, M Corbetta, F Cancelliere, M Giglio Structural Control and Health Monitoring 26 (12), e2451, 2019 | 33 | 2019 |
| In-time UAV flight-trajectory estimation and tracking using Bayesian filters P Banerjee, M Corbetta 2020 IEEE aerospace conference, 1-9, 2020 | 32 | 2020 |
| A particle filter‐based model selection algorithm for fatigue damage identification on aeronautical structures F Cadini, C Sbarufatti, M Corbetta, M Giglio Structural Control and Health Monitoring 24 (11), e2002, 2017 | 28 | 2017 |
| Physics-guided Bayesian neural networks by ABC-SS: Application to reinforced concrete columns J Fernández, J Chiachío, M Chiachío, J Barros, M Corbetta Engineering Applications of Artificial Intelligence 119, 105790, 2023 | 25 | 2023 |
| Application of sparse identification of nonlinear dynamics for physics-informed learning M Corbetta 2020 IEEE Aerospace Conference, 1-8, 2020 | 23 | 2020 |
| On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios M Salvetti, C Sbarufatti, E Cross, M Corbetta, K Worden, M Giglio Mechanical Systems and Signal Processing 123, 84-101, 2019 | 21 | 2019 |
| Sequential Monte Carlo sampling for crack growth prediction providing for several uncertainties M Corbetta, C Sbarufatti, A Manes, M Giglio PHM Society European Conference 2 (1), 2014 | 21 | 2014 |
| A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks RG Nascimento, FAC Viana, M Corbetta, CS Kulkarni Scientific Reports 13 (1), 13856, 2023 | 19 | 2023 |
| Health management and prognostics for electric aircraft powertrain C Kulkarni, M Corbetta 2019 AIAA/IEEE Electric Aircraft Technologies Symposium (EATS), 1-13, 2019 | 17 | 2019 |
| An investigation of strain energy release rate models for real-time prognosis of fiber-reinforced laminates M Corbetta, A Saxena, M Giglio, K Goebel Composite Structures 165, 99-114, 2017 | 16 | 2017 |
| Model-assisted performance qualification of a distributed SHM system for fatigue crack detection on a helicopter tail boom C Sbarufatti, M Corbetta, J San Millan, M Frovel, M Stefaniuk, M Giglio Proceedings of the EWSHM, 2016 | 14 | 2016 |
MARCO GIGLIOProfessore di Costruzione di Macchine, Politecnico di MilanoVerified email at polimi.it
