| Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties G Damblin, M Couplet, B Iooss Journal of Simulation 7 (4), 276-289, 2013 | 196 | 2013 |
| Adaptive numerical designs for the calibration of computer codes G Damblin, P Barbillon, M Keller, A Pasanisi, É Parent SIAM/ASA Journal on Uncertainty Quantification 6 (1), 151-179, 2018 | 34 | 2018 |
| The Uranie platform: an open-source software for optimisation, meta-modelling and uncertainty analysis JB Blanchard, G Damblin, JM Martinez, G Arnaud, F Gaudier EPJ Nuclear Sci. Technol. 5 (4), 2019, 2019 | 32 | 2019 |
| Development of good practice guidance for quantification of thermal-hydraulic code model input uncertainty J Baccou, J Zhang, P Fillion, G Damblin, A Petruzzi, R Mendizábal, ... Nuclear Engineering and Design 354, 110173, 2019 | 31 | 2019 |
| SAPIUM: A generic framework for a practical and transparent quantification of thermal-hydraulic code model input uncertainty J Baccou, J Zhang, P Fillion, G Damblin, A Petruzzi, R Mendizabal, ... Nuclear Science and Engineering 194 (8-9), 721-736, 2020 | 30 | 2020 |
| DiceDesign: Designs of Computer Experiments J Franco, D Dupuy, O Roustant, G Damblin, B Iooss R package version 1, 1617, 2014 | 26* | 2014 |
| Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codes G Damblin, P Gaillard Nuclear Engineering and Design 359, 110391, 2020 | 21 | 2020 |
| SAPIUM a systematic approach for input uncertainty quantification J Baccou, D Bestion, M Couplet, G Damblin, P Fillion, F Fouet, B Iooss, ... BEPU-2018, 2018 | 15 | 2018 |
| Adaptive use of replicated Latin Hypercube Designs for computing Sobol’sensitivity indices G Damblin, A Ghione Reliability Engineering & System Safety 212, 107507, 2021 | 12 | 2021 |
| Bayesian model selection for the validation of computer codes G Damblin, M Keller, P Barbillon, A Pasanisi, E Parent Quality and Reliability Engineering International 32 (6), 2043-2054, 2016 | 12 | 2016 |
| Contributions statistiques au calage et à la validation des codes de calcul G Damblin Paris, AgroParisTech, 2015 | 12 | 2015 |
| A comprehensive Bayesian framework for the development, validation and uncertainty quantification of thermal-hydraulic models R Cocci, G Damblin, A Ghione, L Sargentini, D Lucor Annals of Nuclear Energy 172, 109029, 2022 | 9 | 2022 |
| Application of SAPIUM guidelines to Input Uncertainty Quantification: the ATRIUM project A Ghione, L Sargentini, G Damblin, P Fillion, J Baccou, R Sueur, B Iooss, ... 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics …, 2023 | 7 | 2023 |
| Extension of the CIRCE methodology to improve the Inverse Uncertainty Quantification of several combined thermal-hydraulic models R Cocci, G Damblin, A Ghione, L Sargentini, D Lucor Nuclear Engineering and Design 398, 111974, 2022 | 6 | 2022 |
| Approche décisionnelle bayésienne pour estimer une courbe de fragilité G Damblin, M Keller, A Pasanisi, P Barbillon, E Parent Journal de la Société Française de Statistique 155 (3), 78-103, 2014 | 6 | 2014 |
| A generalization of the CIRCE method for quantifying input model uncertainty in presence of several groups of experiments G Damblin, F Bachoc, S Gazzo, L Sargentini, A Ghione Nuclear Engineering and Design 413, 112527, 2023 | 3 | 2023 |
| Model assessment for direct contact condensation induced by a sub-cooled water jet in a circular pipe R Cocci, A Ghione, L Sargentini, G Damblin, D Lucor International Journal of Heat and Mass Transfer 195, 123162, 2022 | 3 | 2022 |
| Sensitivity analysis on the critical mass flowrate based on Sobol’indices through replicated LHS L Sargentini, G Damblin 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics …, 2019 | 3 | 2019 |
| Development of a systematic approach for quantification of thermal hydraulic code model input uncertainty J Baccou, J Zhang, P Fillion, G Damblin, A Petruzzi, R Mendizábal, ... Proceedings of NURETH-2019, Portland, Oregon, USA, 2019 | 3 | 2019 |
| A Bayesian framework for quantifying the uncertainty of physical models integrated into thermal-hydraulic computer codes G Damblin BEST ESTIMATE PLUS UNCERTAINTY CONFERENCE, 2019 | 3 | 2019 |
