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Brian Staber
Brian Staber
ML engineer, Safran Tech
Verified email at safrangroup.com - Homepage
Title
Cited by
Cited by
Year
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
542019
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
512018
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
382017
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
382017
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
372017
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
362015
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
152015
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
142017
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
B Staber, S Da Veiga
arXiv preprint arXiv:2206.06779, 2022
4*2022
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
22021
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
12024
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
12024
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
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Articles 1–16