| Bayesian optimization using deep Gaussian processes with applications to aerospace system design A Hebbal, L Brevault, M Balesdent, EG Talbi, N Melab Optimization and Engineering 22, 321-361, 2021 | 60* | 2021 |
| Multi-objective multidisciplinary design optimization approach for partially reusable launch vehicle design L Brevault, M Balesdent, A Hebbal Journal of Spacecraft and Rockets 57 (2), 373-390, 2020 | 32 | 2020 |
| Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities, application to aerospace systems L Brevault, M Balesdent, A Hebbal Aerospace Science and Technology 107, 106339, 2020 | 27 | 2020 |
| Multi-fidelity modeling with different input domain definitions using deep Gaussian processes A Hebbal, L Brevault, M Balesdent, EG Talbi, N Melab Structural and Multidisciplinary Optimization 63, 2267-2288, 2021 | 21 | 2021 |
| Multi-objective optimization using deep Gaussian processes: application to aerospace vehicle design A Hebbal, L Brevault, M Balesdent, EG Talbi, N Melab AIAA Scitech 2019 Forum, 1973, 2019 | 21 | 2019 |
| Efficient global optimization using deep gaussian processes A Hebbal, L Brevault, M Balesdent, EG Taibi, N Melab 2018 IEEE Congress on evolutionary computation (CEC), 1-8, 2018 | 21 | 2018 |
| Surrogate model-based multi-objective MDO approach for partially Reusable Launch Vehicle design L Brevault, M Balesdent, A Hebbal, A Patureau De Mirand AIAA Scitech 2019 Forum, 0704, 2019 | 6 | 2019 |
| Multi-fidelity modeling using DGPs: Improvements and a generalization to varying input space dimensions A Hebbal, L Brevault, M Balesdent, EG Talbi, N Melab | 5 | 2019 |
| Deep Gaussian processes for the analysis and optimization of complex systems-application to aerospace system design A Hebbal Université de Lille, 2021 | 3 | 2021 |
| Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities L Brevault, M Balesdent, A Hebbal arXiv preprint arXiv:2006.16728, 2020 | 3 | 2020 |
| Deep Gaussian process for multi-objective Bayesian optimization A Hebbal, M Balesdent, L Brevault, N Melab, EG Talbi Optimization and Engineering, 1-40, 2022 | 2 | 2022 |
| Mdo related issues: Multi-objective and mixed continuous/discrete optimization L Brevault, M Balesdent, J Morio, L Brevault, J Pelamatti, A Hebbal, ... Aerospace system analysis and optimization in uncertainty, 321-358, 2020 | 2 | 2020 |
| Expendable and Reusable Launch Vehicle Design L Brevault, M Balesdent, J Morio, L Brevault, M Balesdent, A Hebbal Aerospace System Analysis and Optimization in Uncertainty, 421-476, 2020 | 1 | 2020 |
| Processus gaussiens profonds pour l’analyse et l’optimisation des systèmes complexes: application à la conception des systèmes aérospatiaux A Hebbal | | 2021 |
| Multi-Disciplinary Design Multi-Objective Optimization of Aerospace Vehicles using Surrogate Models A Hebbal, L Brevault, M Balesdent, EG Talbi, N Melab OLA 2018-International Workshop on Optimization and Learning: Challenges and …, 2018 | | 2018 |
| Efficient Global Optimization using Deep Gaussian Processes A Hebbal, L Brevault, M Balesdent, EG Talbi, N Melab | | |
| Springer Optimization and Its Applications PT Sums | | |
Loïc BrevaultONERAVerified email at onera.fr
