Cai, J., Angeli, P.-E., Martinez, J.-M., Damblin, G. and Lucor, D. (2024) Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows Computers and Fluids, https://doi.org/10.1016/j.compfluid.2024.106246

Sundar, R., Lucor, D. and Sarkar, S.(2024) Understanding the training of PINNs for unsteady flow past a plunging foil through the lens of input subdomain level loss function gradients arXiv:2402.17346 [physics.flu-dyn]

Bouaou, K., Dietenbeck, T., Soulat, G., Bargiotas, I., Houriez‐Gombaud‐Saintonge, S., De Cesare, A., Gencer, U., Giron, A., Jiménez, E., Messas, E., Lucor, D., Bollache, E., Mousseaux, E. & Kachenoura, N. (2024) 4D flow MRI aortic cross-sectional pressure changes and their associations with flow patterns in health and aneurysm Journal of Cardiovascular Magnetic Resonance (accepted)

Jaber, E., Blot, V., Brunel, N., Chabridon, V., Remy, E., Iooss, B., Lucor, D., Mougeot, M. and Leite, A. (2024) Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees, arXiv:2401.07733

Jaber, E., Chabridon, V., Remy, E., Baudin, M., Lucor, D., Mougeot, M. and Iooss, B. (2024) Sensitivity analyses of a multi-physics long-term clogging model for steam generators, International Journal for Uncertainty Quantification (accepted)

Sundar, R., Majumdar, D., Lucor, D. and Sarkar, S.(2024) Physics-informed neural networks modelling for systems with moving immersed boundaries: Application to an unsteady flow past a plunging foil Journal of Fluids and Structures, 125, 104066

Coelho, L., Fagiano, C., Julien, C., Fabbiane, N., Lucor, D. (2023) Multi-scale approach for Reliability-based Design Optimization with metamodel upscaling, Structural and Multidisciplinary Optimization, 66, 205 https://doi.org/10.1007/s00158-023-03643-4

Cheng, S., Quilodran-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., Xiao, D., Janjic, T., Ding, W., Guo, Y., Carrassi, A., Bocquet, M. and Arcucci, R., (2023) Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review IEEE/CAA Journal of Automatica Sinica, 10(6), pp. 1361-1387, doi: 10.1109/JAS.2023.123537

Sundar, R., Majumdar, D., Lucor, D. and Sarkar, S.(2023) Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil arXiv:2306.13395

Nony, B. X., Rochoux, M., C., Jaravel, T. and Lucor, D. (2023) Reduced-order model for microscale atmospheric dispersion combining multi-fidelity LES and RANS data, ECCOMAS Proceedings ISBN: 978-618-5827-02-1, p.265-283, UNCECOMP 2023

Nony, B. X., Rochoux, M. C., Jaravel, T., Lucor, D. (2023) Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion SERRA

Cocci, R., Damblin, G., Ghione, A., Sargentini, L. and Lucor, D. (2022) Extension of the CIRCE methodology to improve the inverse uncertainty quantification of several combined thermal-hydraulic models Nuclear Engineering and Design, 398:111974

Kronborg, J., Svelander, F., Eriksson-Lidbrink, S., Ludvig Lindström, L. , Homs-Pons, C., Lucor, D. and Hoffman, J. (2022) Computational Analysis of Flow Structures in Turbulent Ventricular Blood Flow Associated With Mitral Valve Intervention Frontiers in Physiology, Sec. Computational Physiology and Medicine, https://doi.org/10.3389/fphys.2022.806534

Cai, J., Angeli, P.-E., Martinez, J.-M., Damblin, G. and Lucor, D. (2022) Reynolds Stress Anisotropy Tensor Predictions for Turbulent Channel Flow using Neural Networks , arXiv:2208.14301

Cocci, R., Ghione, A., Sargentini, L., Damblin, G. and Lucor, D., (2022) Model assessment for direct contact condensation induced by a sub-cooled water jet in a circular pipe International Journal of Heat and Mass Transfer, 195:123162

Coelho, L., Fabbiane, N., Fagiano, C., Julien, C., Lucor, D., (2022) Optimisation de stratifiés composites sous contrainte fiabiliste à travers un double espace de design CSMA 2022, 15eme Colloque National en Calcul des Structures, 7p

Nony, B. X., Rochoux, M., Jaravel, T. and Lucor, D., (2022) Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion arXiv preprint, arXiv:2208.01518

Cocci, R., Damblin, G., Ghione, A., Sargentini, L., Lucor, D., (2022) A comprehensive Bayesian framework for the development, validation and uncertainty quantification of thermal-hydraulic models Annals of Nuclear Energy, 172:109029

Lucor, D., Agrawal, A., Sergent, A. (2022) Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection Journal of Computational Physics, 456:111022

Méndez Rojano, R., Zhussupbekov, M., Antaki, J., F., Lucor, D. (2022) Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100 International Journal for Numerical Methods in Biomedical Engineering, 38, 5

El Garroussi, S., Ricci, S., De Lozzo, M., Goutal, N., Lucor, D. (2021) Tackling random fields non-linearities with unsupervised clustering of polynomial chaos expansion in latent space: application to global sensitivity analysis of river flooding SERRA, 36, p. 693–718

Lucor, D., Agrawal, A., Sergent, A. (2021) Physics-aware deep neural networks for surrogate modeling of turbulent natural convection arXiv.org (preprint)

Cheng, S., Lucor, D., Argaud, J.P. (2021) Observation data compression for variational assimilation of dynamical systems Journal of Computational Science, 53:101405

Cheng, S., Argaud, J.P., Iooss, B., Ponçot, A. & Lucor, D. (2021) A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping Mathematical Geosciences

Cheng, S., Argaud, J.P., Iooss, B., Lucor, D. & Ponçot, A. (2020) Error covariance tuning in variational data assimilation: application to an operating hydrological model SERRA

Gineau, A., Longatte, E., Lucor, D. & Sagaut, P. (2020) Macroscopic model of fluid structure interaction in cylinder arrangement using theory of mixture Computers & Fluids 202: 104499

Bouaou, K., Dietenbeck, T., Soulat, G., Houriez‐Gombaud‐Saintonge, S., Bargiotas, I., De Cesare, A., Gencer, U., Giron, A., Redheuil, E., Bollache, E., Lucor, D., Mousseaux, E. & Kachenoura, N. (2020) Aortic Pressure Behind Flow Disorganization in Aneurismal Aorta: a Magnetic Resonance Imaging Study Artery Research 25(Issue supplement 1)

Cheng, S., Argaud, J.P., Iooss, B., Lucor, D. & Ponçot, A. (2019) Background Error Covariance Iterative Updating with Invariant Observation Measures for Data Assimilation SERRA 33: 2033-2051

El Garroussi, S., De Lozzo, M., Ricci, S., Lucor, D. Goutal, N. , Goeury, C., Boyaval, S. (2019) Uncertainty quantification in a two-dimensional river hydraulic model ECCOMAS Proceedia, UNCECOMP, p243-262

Adjoua, O., Pitre-Champagnat, S. Lucor, D. (2019) Reduced-order modeling of hemodynamics across macroscopic through mesoscopic circulation scales International Journal for Numerical Methods in Biomedical Engineering 35(12)

Méndez Rojano, R., Mendez, S., Lucor, D., Ranc, A., Giansily-Blaizot, M., Schved, J.-F., & Nicoud, F. (2019) Kinetics of the coagulation cascade including the contact activation system: sensitivity analysis and model reduction Biomechanics and Modeling in Mechanobiology 18(4): 1139-1153

Bouaou, K., Bargiotas, I., Dietenbeck, T., Bollache, E., Soulat, G., Craiem, D., Houriez‐Gombaud‐Saintonge, S., De Cesare, A., Gencer, U., Giron, A., Redheuil, A., Messas, E., Lucor, D., Mousseaux, E. & Kachenoura, N. (2019) Analysis of aortic pressure fields from 4D flow MRI in healthy volunteers: Associations with age and left ventricular remodeling Journal of Magnetic Resonance Imaging 50(3): 982-993

Van Langenhove, J., Lucor, D., Alauzet, F. & Belme, A. (2018) Goal-oriented error control of stochastic system approximations using metric-based anisotropic adaptations Journal of Computational Physics 374:384-412

El Moçayd, N., Ricci, S., Goutal, N., Rochoux, M. C., Boyaval, S., Goeury, C., Lucor, D., Thual, O. (2018) Polynomial Surrogates for Open-Channel Flows in Random Steady State Environmental Modeling and Assessment, 23(3)

Chassaing, J.C., Nitschke, C.T., Vincenti, A., Cinnella, P., Lucor, D.. (2018) Advances in Parametric and Model-Form Uncertainty Quantification in Canonical Aeroelastic Systems AerospaceLab journal (ONERA), 1-19

Rochoux, M.C., Collin, A., Zhang, C., Trouvé, A., Lucor, D., Moireau, P. (2018) Front shape similarity measure for shape-oriented sensitivity analysis and data assimilation for Eikonal equation ESAIM: Proceedings and Surveys 63, 258-279

Lucor, D., Le Maître, O. P. (2018) Cardiovascular modeling with adapted parametric inference ESAIM: Proceedings and Surveys 62, 91-107

Brault, A., Dumas, L. Lucor, D. (2017) Uncertainty quantification of inflow boundary condition and proximal arterial stiffness–coupled effect on pulse wave propagation in a vascular network International journal for numerical methods in biomedical engineering 33(10)

Dumas, L., El Bouti, T., Lucor, D. (2017) A Robust and Subject-Specific Hemodynamic Model of the Lower Limb Based on Noninvasive Arterial Measurements Journal of Biomechanical Engineering 30: 139(1)

Resmini, A., Peter, J., & Lucor, D. (2017) Mono-block and non-matching multi-block structured mesh adaptation based on aerodynamic functional total derivatives for RANS flow International Journal for Numerical Methods in Fluids, 83(11)

Nitschke, C. T., Cinnella, P., Lucor, D., & Chassaing, J.-C. (2017) Model-form and predictive uncertainty quantification in linear aeroelasticity Journal of Fluids and Structures, 73

Bouaou, K., Bargiotas, I., Craiem, D., Soulat, G., Dietenbeck, T., Houriez‐Gombaud‐Saintonge, S., De Cesare, A., Gencer, U., Giron, A., Redheuil, A., Lucor, D., Mousseaux, E. & Kachenoura, N. (2017) Relative aortic blood pressure using 4D flow MRI: Associations with age and aortic tapering Computing in Cardiology (CinC), 1-4

Van Langenhove, J. W., Lucor, D., & Belme, A. (2016) Robust uncertainty quantification using preconditioned least-squares polynomial approximations with L1-regularization International Journal for Uncertainty Quantification, 6(1)

Resmini, A., Peter, J., & Lucor, D. (2016) Sparse grids-based stochastic approximations with applications to aerodynamics sensitivity analysis International Journal for Numerical Methods in Engineering, 106(1)

Pöette, G., Birolleau, A., & Lucor, D. (2015) Iterative polynomial approximation adapting to arbitrary probability distribution SIAM Journal on Numerical Analysis, 53(3)

Nitschke, C. T., Maruani, J., Vincenti, A., Lucor, D., & Chassaing, J.-C. (2015) Uncertainty quantification in aeroelastic response of an idealized composite wing In International Forum on Aeroelasticity and Structural Dynamics, IFASD

Bollache, E., Kachenoura, N., Bargiotas, I., Giron, A., De Cesare, A., Bensalah, M., Lucor, D., Redheuil, A., Mousseaux, E. (2015) How to estimate aortic characteristic impedance from magnetic resonance and applanation tonometry data? Journal of Hypertension, 33(3)

Birolleau, A., Poëtte, G., & Lucor, D. (2014) Adaptive bayesian inference for discontinuous inverse problems, application to hyperbolic conservation laws Communications in Computational Physics, 16(1)

Bollache, E., Kachenoura, N., Redheuil, A., Frouin, F., Mousseaux, E., Recho, P.. Lucor, D., (2014) Descending aorta subject-specific one-dimensional model validated against in vivo data Journal of Biomechanics, 47(2), 424-431

Rochoux, M. C., Ricci, S., Lucor, D., Cuenot, B., & Trouvé, A. (2014) Towards predictive data-driven simulations of wildfire spread - Part I: Reduced-cost ensemble Kalman filter based on a polynomial chaos surrogate model for parameter estimation Natural Hazards and Earth System Sciences, 14(11)

Editors: Hester, B., Lucor, D., Mishra, S., Schwab, C. (2013) Uncertainty Quantification in Computational Fluid Dynamics Lecture Notes in Computational Science and Engineering book series (LNCSE), volume 9, Springer

Després, B. Poëtte, G., Lucor, D. (2013) Robust uncertainty propagation in systems of conservation laws with the entropy closure method in Uncertainty Quantification in Computational Fluid Dynamics, chapter in Lecture Notes in Computational Science and Engineering book series (LNCSE), volume 9, 105-149