B. Staber

发表

Stochastic hyperelastic constitutive laws and identification procedure for soft biological tissues with intrinsic variability.

B. Staber, J. Guilleminot, B. Staber, 2017, Journal of the mechanical behavior of biomedical materials.

Stochastic modeling of the Ogden class of stored energy functions for hyperelastic materials: the compressible case

Brian Staber, Johann Guilleminot, J. Guilleminot, 2017 .

Functional approximation and projection of stored energy functions in computational homogenization of hyperelastic materials: A probabilistic perspective

J. Guilleminot, B. Staber, 2017 .

Stochastic modeling and generation of random fields of elasticity tensors: A unified information-theoretic approach

Brian Staber, Johann Guilleminot, J. Guilleminot, 2017 .

Quantitative performance evaluation of Bayesian neural networks

S. D. Veiga, B. Staber, S. Veiga, 2022, ArXiv.

Quantitative performance evaluation of Bayesian neural networks

S. D. Veiga, B. Staber, 2022, ArXiv.

A random field model for anisotropic strain energy functions and its application for uncertainty quantification in vascular mechanics

Johann Guilleminot, B. Staber, J. Guilleminot, 2018 .

Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties

Brian Staber, Johann Guilleminot, J. Guilleminot, 2015 .

Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites

Christian Soize, John G. Michopoulos, Athanasios Iliopoulos, 2019, Computer Methods in Applied Mechanics and Engineering.

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability

F. Casenave, Xavier Roynard, B. Staber, 2023, ArXiv.

Benchmarking Bayesian neural networks and evaluation metrics for regression tasks

B. Staber, S. Veiga, 2022, 2206.06779.

Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization

B. Staber, S. Veiga, Cl'ement B'enard, 2023, 2301.13528.

Approximate Solutions of Lagrange Multipliers for Information-Theoretic Random Field Models

Brian Staber, Johann Guilleminot, J. Guilleminot, 2015, SIAM/ASA J. Uncertain. Quantification.

MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability

F. Casenave, Xavier Roynard, B. Staber, 2023, ArXiv.

Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization

B. Staber, S. Veiga, Cl'ement B'enard, 2023, NeurIPS.