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Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

Computer methods in applied mechanics and engineering, 2021-07, Vol.380, p.113777, Article 113777

2021

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Random matrix theory for modeling uncertainties in computational mechanics

Computer methods in applied mechanics and engineering, 2005-04, Vol.194 (12-16), p.1333-1366

Soize, C.

2005

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A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics

Journal of sound and vibration, 2005-12, Vol.288 (3), p.623-652

Soize, C.

2005

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Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data

Computer methods in applied mechanics and engineering, 2010-07, Vol.199 (33), p.2150-2164

Soize, C.

2010

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Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Computational mechanics, 2018-09, Vol.62 (3), p.477-497

Soize, C.

2018

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A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension

Computer methods in applied mechanics and engineering, 2011-01, Vol.200 (45), p.3083-3099

Soize, C.

2011

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Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics

International journal for numerical methods in engineering, 2019-02, Vol.117 (7), p.819-843

2019

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A nonparametric probabilistic approach for quantifying uncertainties in low‐dimensional and high‐dimensional nonlinear models

International journal for numerical methods in engineering, 2017-02, Vol.109 (6), p.837-888

2017

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Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators

Computer methods in applied mechanics and engineering, 2006, Vol.195 (1), p.26-64

Soize, C.

2006

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Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials

Computer methods in applied mechanics and engineering, 2013-02, Vol.254, p.61-82

2013

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

Computer methods in applied mechanics and engineering, 2019-04, Vol.347, p.425-444

2019

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Computational nonlinear stochastic homogenization using a nonconcurrent multiscale approach for hyperelastic heterogeneous microstructures analysis

International journal for numerical methods in engineering, 2012-08, Vol.91 (8), p.799-824

2012

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Stochastic elliptic operators defined by non-Gaussian random fields with uncertain spectrum

Theory of probability and mathematical statistics, 2021-01, Vol.105, p.113-136

Soize, C.

2021

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Polynomial chaos representation of databases on manifolds

Journal of computational physics, 2017-04, Vol.335, p.201-221

2017

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A Novel Source-Filter Stochastic Model for Voice Production

Journal of voice, 2023-01, Vol.37 (1), p.1-8

2023

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Identification and sampling of Bayesian posteriors of high-dimensional symmetric positive-definite matrices for data-driven updating of computational models

Computer methods in applied mechanics and engineering, 2019-08, Vol.352, p.300-323

2019

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Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite

Computer methods in applied mechanics and engineering, 2016-05, Vol.303, p.430-449

2016

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Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

Computers & structures, 2013-09, Vol.126, p.41-55

Soize, C.

2013

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Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures

Computer methods in applied mechanics and engineering, 2016-06, Vol.305, p.154-172

2016

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Updating an uncertain and expensive computational model in structural dynamics based on one single target FRF using a probabilistic learning tool

Computational mechanics, 2023-06, Vol.71 (6), p.1161-1177

2023

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Tensor-valued random fields for meso-scale stochastic model of anisotropic elastic microstructure and probabilistic analysis of representative volume element size

Probabilistic engineering mechanics, 2008-04, Vol.23 (2), p.307-323

Soize, C.

2008

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Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices

Journal of biomechanics, 2018-06, Vol.74, p.126-133

2018

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Non-linear dynamics of a drill-string with uncertain model of the bit–rock interaction

International journal of non-linear mechanics, 2009-10, Vol.44 (8), p.865-876

2009

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A nonparametric model of random uncertainties for reduced matrix models in structural dynamics

Probabilistic engineering mechanics, 2000-07, Vol.15 (3), p.277-294

Soize, C.

2000

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Identification of Bayesian posteriors for coefficients of chaos expansions

Journal of computational physics, 2010-05, Vol.229 (9), p.3134-3154

2010

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Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

Random matrix theory for modeling uncertainties in computational mechanics

A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics

Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension

Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics

A nonparametric probabilistic approach for quantifying uncertainties in low‐dimensional and high‐dimensional nonlinear models

Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators

Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials

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

Computational nonlinear stochastic homogenization using a nonconcurrent multiscale approach for hyperelastic heterogeneous microstructures analysis

Stochastic elliptic operators defined by non-Gaussian random fields with uncertain spectrum

Polynomial chaos representation of databases on manifolds

A Novel Source-Filter Stochastic Model for Voice Production

Identification and sampling of Bayesian posteriors of high-dimensional symmetric positive-definite matrices for data-driven updating of computational models

Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite

Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures

Updating an uncertain and expensive computational model in structural dynamics based on one single target FRF using a probabilistic learning tool

Tensor-valued random fields for meso-scale stochastic model of anisotropic elastic microstructure and probabilistic analysis of representative volume element size

Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices

Non-linear dynamics of a drill-string with uncertain model of the bit–rock interaction

A nonparametric model of random uncertainties for reduced matrix models in structural dynamics

Identification of Bayesian posteriors for coefficients of chaos expansions

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