• 
  Mohammad Daffa Robani, Paul Saves, Lavi Rizki Zuhal, Pramudita Satria Palar, Joseph Morlier. SMT-EX: AnExplainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration. AIAA SciTech 2025, AIAA, Jan 2025, Orlando, FL, United States. ⟨hal-04816698⟩
• 
  Paul Saves, Jasper Bussemaker, Rémi Lafage, Thierry Lefebvre, Nathalie Bartoli, et al.. System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility. ODAS 2024: 24th joint ONERA-DLR Aerospace Symposium, DLR, Jun 2024, Brunschweig, Germany. ⟨hal-04590323⟩
• 
  Renaud Ferrier, Mohamed Larbi Kadri, Sylvain Drapier, Pierre Gosselet. Apprentissage automatique d'EDPs contraintes par la physique pour l'identification des hétérogénéités dans les structures mécaniques élancées. 16ème Colloque National en Calcul de Structures (CSMA 2024), CNRS; CSMA; ENS Paris-Saclay; CentraleSupélec, May 2024, Hyères, France. ⟨hal-04230541⟩
• 
  Bingqian Li, Ludovic Cauvin, Piotr Breitkopf, Jianqiang Jin. Réduction de la dimensionnalité de textures de métaux polycristallins. 16ème Colloque National en Calcul de Structures (CSMA 2024), CNRS; CSMA; ENS Paris-Saclay; CentraleSupélec, May 2024, Hyères, France. ⟨hal-04611025⟩
• 
  Antoine Benady, Emmanuel Baranger, Ludovic Chamoin. Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified Constitutive Relation Error framework.. Computer Methods in Applied Mechanics and Engineering, 2024, 425, pp.116967. ⟨10.1016/j.cma.2024.116967⟩. ⟨hal-04368755⟩
• 
  Héloïse Dandin, Adrien Leygue, Laurent Stainier. Graph-based representation of history-dependent material response in the Data-Driven Computational Mechanics framework. Computer Methods in Applied Mechanics and Engineering, 2024, 419, pp.116694. ⟨10.1016/j.cma.2023.116694⟩. ⟨hal-04259707v3⟩
• 
  Antoine Benady, Emmanuel Baranger, Ludovic Chamoin. NN-mCRE: a modified Constitutive Relation Error framework for unsupervised learning of nonlinear state laws with physics-augmented Neural Networks. International Journal for Numerical Methods in Engineering, 2024, ⟨10.1002/nme.7439⟩. ⟨hal-04102108v2⟩
• 
  Paul Saves, Rémi Lafage, Nathalie Bartoli, Youssef Diouane, Jasper Bussemaker, et al.. SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes. Advances in Engineering Software, 2024, 188 (103571), pp.103571. ⟨10.1016/j.advengsoft.2023.103571⟩. ⟨hal-04331916⟩
• 
  Paul Saves, Youssef Diouane, Nathalie Bartoli, Thierry Lefebvre, Joseph Morlier. High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft. Structural and Multidisciplinary Optimization, 2024, 67 (5), pp.81. ⟨10.1007/s00158-024-03785-z⟩. ⟨hal-04574274⟩
• 
  Pramudita Satria Palar, Yohanes Bimo Dwianto, Lavi Rizki Zuhal, Joseph Morlier, Koji Shimoyama, et al.. Multi-objective design space exploration using explainable surrogate models. Structural and Multidisciplinary Optimization, 2024, 67 (38), ⟨10.1007/s00158-024-03769-z⟩. ⟨hal-04512428⟩
• 
  Bingqian Li, Piotr Breitkopf, Ludovic Cauvin, Balaji Raghavan. Model reduction in the context of polycrystalline plasticity. MORTech 2023 – 6th International Workshop on Model Reduction Techniques, Nov 2023, Paris-Saclay, France. ⟨hal-04403924⟩
• 
  Rémy Charayron, Thierry Lefebvre, Nathalie Bartoli, Joseph Morlier. Towards a multi-fidelity & multi-objective Bayesian optimization efficient algorithm. Aerospace Science and Technology, 2023, 142 (Part B), ⟨10.1016/j.ast.2023.108673⟩. ⟨hal-04254361⟩
• 
  Benoit Hilloulin, Abdelhamid Hafidi, Sonia Boudache, Ahmed Loukili. Interpretable ensemble machine learning for the prediction of the expansion of cementitious materials under external sulfate attack. Journal of Building Engineering, 2023, pp.107951. ⟨10.1016/j.jobe.2023.107951⟩. ⟨hal-04255480⟩
• 
  Edgar Zembra, Antoine Benady, Emmanuel Baranger, Ludovic Chamoin. Use of physics-augmented neural networks for unsupervised learning of material constitutive relations - Comparison of the NN-Euclid and NN-mCRE methods. ENS Paris-Saclay; Centrale Supélec. 2023. ⟨hal-04255767⟩
• 
  Nathalie Bartoli, Thierry Lefebvre, Rémi Lafage, Paul Saves, Youssef Diouane, et al.. Multi-objective bayesian optimization with mixed-categorical design variables for expensive-to-evaluate aeronautical applications. AEROBEST 2023, ECCOMAS, Jul 2023, Lisbonne, Portugal. pp.436. ⟨hal-04170287⟩
• 
  Abdelhamid Hafidi, Benoit Hilloulin, Sonia Boudache, Umunnakwe Rejoice, Ahmed Loukili. Comparison of Machine Learning Algorithms for the Prediction of the External Sulphate Attack Resistance of Blended Cements. International RILEM Conference on Synergising Expertise towards Sustainability and Robustness of Cement-based Materials and Concrete Structures. SynerCrete 2023., Jun 2023, Milos Island, Greece. pp.725-735, ⟨10.1007/978-3-031-33187-9_67⟩. ⟨hal-04131990⟩
• 
  Benoit Hilloulin, van Quan Tran. Interpretable machine learning model for autogenous shrinkage prediction of low-carbon cementitious materials. Construction and Building Materials, 2023, 396, pp.132343. ⟨10.1016/j.conbuildmat.2023.132343⟩. ⟨hal-04153200⟩
• 
  Paul Saves, Y. Diouane, Nathalie Bartoli, T. Lefebvre, J. Morlier. A mixed-categorical correlation kernel for Gaussian process. Neurocomputing, In press, ⟨10.1016/j.neucom.2023.126472⟩. ⟨hal-04130877v2⟩
• 
  Afsal Pulikkathodi, Elisabeth Longatte-Lacazedieu, Ludovic Chamoin, Juan-Pedro Berro Ramirez, Laurent Rota, et al.. A neural network-based data-driven local modeling of spotwelded plates under impact. Mechanics & Industry, 2023, 24, pp.34. ⟨10.1051/meca/2023029⟩. ⟨hal-04190457⟩
• 
  Paul Saves, Youssef Diouane, Nathalie Bartoli, Thierry Lefebvre, Joseph Morlier. A general square exponential kernel to handle mixed-categorical variables for Gaussian process. AIAA AVIATION 2022 Forum, Jun 2022, Chicago (virtual), France. ⟨10.2514/6.2022-3870⟩. ⟨hal-03700850⟩

27

1