| Application of reduced-order models based on PCA & Kriging for the development of digital twins of reacting flow applications G Aversano, A Bellemans, Z Li, A Coussement, O Gicquel, A Parente Computers & chemical engineering 121, 422-441, 2019 | 83 | 2019 |
| Digital twin of a combustion furnace operating in flameless conditions: reduced-order model development from CFD simulations G Aversano, M Ferrarotti, A Parente Proceedings of the Combustion Institute 38 (4), 5373-5381, 2021 | 51 | 2021 |
| Optimization of chemical kinetics for methane and biomass pyrolysis products in moderate or intense low-Oxygen dilution combustion M Fürst, P Sabia, M Lubrano Lavadera, G Aversano, M De Joannon, ... Energy & fuels 32 (10), 10194-10201, 2018 | 27 | 2018 |
| Feature extraction and reduced-order modelling of nitrogen plasma models using principal component analysis A Bellemans, G Aversano, A Coussement, A Parente Computers & chemical engineering 115, 504-514, 2018 | 25 | 2018 |
| Data-driven fluid mechanics: combining first principles and machine learning MA Mendez, A Ianiro, BR Noack, SL Brunton Cambridge University Press, 2023 | 24 | 2023 |
| Impact of the partitioning method on multidimensional adaptive-chemistry simulations G D’Alessio, A Cuoci, G Aversano, M Bracconi, A Stagni, A Parente Energies 13 (10), 2567, 2020 | 22 | 2020 |
| A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps D de Andres, W Cui, F Ruppin, M De Petris, G Yepes, G Gianfagna, ... Nature Astronomy 6 (11), 1325-1331, 2022 | 18 | 2022 |
| PCA and Kriging for the efficient exploration of consistency regions in Uncertainty Quantification G Aversano, JC Parra-Alvarez, BJ Isaac, ST Smith, A Coussement, ... Proceedings of the Combustion Institute 37 (4), 4461-4469, 2019 | 17 | 2019 |
| Combination of polynomial chaos and Kriging for reduced-order model of reacting flow applications G Aversano, G D’Alessio, A Coussement, F Contino, A Parente Results in Engineering 10, 100223, 2021 | 16 | 2021 |
| Surrogate-assisted modeling and robust optimization of a micro gas turbine plant with carbon capture S Giorgetti, D Coppitters, F Contino, WD Paepe, L Bricteux, G Aversano, ... Journal of Engineering for Gas Turbines and Power 142 (1), 011010, 2020 | 15 | 2020 |
| Advancing reacting flow simulations with data-driven models K Zdybał, G D'Alessio, G Aversano, MR Malik, A Coussement, ... arXiv preprint arXiv:2209.02051, 2022 | 11 | 2022 |
| Mic: Multi-view image classifier using generative adversarial networks for missing data imputation G Aversano, M Jarraya, M Marwani, I Lahouli, S Skhiri 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD …, 2021 | 4 | 2021 |
| Application of reducedorder models based on the combination of pca & kriging on 1d flames G Aversano, A Parente, O Gicquel, A Coussement Fuel, 2017 | 3 | 2017 |
| Model reduction by PCA and Kriging G Aversano, Z Li, O Gicquel, A Parente International conference of computational methods in sciences and engineering, 2018 | 2 | 2018 |
| PCA & Kriging for Surrogate Models G Aversano, A Parente | 1 | 2016 |
| PCA & Kriging for model reduction G Aversano, A Parente Rapp. tech. The Combustion Institute, 2016 | 1 | 2016 |
| SANGEA: Scalable and Attributed Network Generation V Lemaire, Y Achenchabe, L Ody, HE Souid, G Aversano, N Posocco, ... Asian Conference on Machine Learning, 678-693, 2024 | | 2024 |
| " Link prediction on CV graphs: a temporal graph neural network approach N Farnoodian, S Nijssen, G Aversano | | 2022 |
| Development of physics-based reduced-order models for reacting flow applications G Aversano Université Paris Saclay (COmUE); Université libre de Bruxelles (1970-....), 2019 | | 2019 |
| Effects of the training dataset and data preprocessing on adaptive chemistry simulations G D'Alessio, G Aversano, A Cuoci, A Parente | | 2019 |
