| Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites B Staber, J Guilleminot, C Soize, J Michopoulos, A Iliopoulos Computer Methods in Applied Mechanics and Engineering 347, 425-444, 2019 | 56 | 2019 |
| A random field model for anisotropic strain energy functions and its application for uncertainty quantification in vascular mechanics B Staber, J Guilleminot Computer Methods in Applied Mechanics and Engineering 333, 94-113, 2018 | 52 | 2018 |
| Stochastic hyperelastic constitutive laws and identification procedure for soft biological tissues with intrinsic variability B Staber, J Guilleminot Journal of the mechanical behavior of biomedical materials 65, 743-752, 2017 | 39 | 2017 |
| Stochastic modeling of the Ogden class of stored energy functions for hyperelastic materials: the compressible case B Staber, J Guilleminot ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte …, 2017 | 38 | 2017 |
| Stochastic modeling and generation of random fields of elasticity tensors: a unified information-theoretic approach B Staber, J Guilleminot Comptes Rendus. Mécanique 345 (6), 399-416, 2017 | 38 | 2017 |
| Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties B Staber, J Guilleminot Comptes Rendus. Mécanique 343 (9), 503-514, 2015 | 37 | 2015 |
| Approximate solutions of Lagrange multipliers for information-theoretic random field models B Staber, J Guilleminot SIAM/ASA Journal on Uncertainty Quantification 3 (1), 599-621, 2015 | 15 | 2015 |
| Functional approximation and projection of stored energy functions in computational homogenization of hyperelastic materials: A probabilistic perspective B Staber, J Guilleminot Computer Methods in Applied Mechanics and Engineering 313, 1-27, 2017 | 14 | 2017 |
| Benchmarking Bayesian neural networks and evaluation metrics for regression tasks B Staber, S Da Veiga arXiv preprint arXiv:2206.06779, 2022 | 4* | 2022 |
| Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization C Bénard, B Staber, S Da Veiga Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Loss of ellipticity analysis in non-smooth plasticity B Staber, S Forest, M Al Kotob, M Mazière, T Rose International Journal of Solids and Structures 222, 111010, 2021 | 2 | 2021 |
| MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability F Casenave, B Staber, X Roynard Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels RC Perez, S Da Veiga, J Garnier, B Staber arXiv preprint arXiv:2402.03838, 2024 | | 2024 |
| Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels R Carpintero Perez, S da Veiga, J Garnier, B Staber arXiv e-prints, arXiv: 2402.03838, 2024 | | 2024 |
| Stochastic analysis, simulation and identification of hyperelastic constitutive equations B Staber Université Paris-Est, 2018 | | 2018 |
| Analyse stochastique, simulation et identification de lois de comportement hyperélastiques B Staber | | |
