Search Results - "Soize, C."

Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets by Soize, C., Ghanem, R.

“…A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear…”
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Random matrix theory for modeling uncertainties in computational mechanics by Soize, C.

“…This paper deals with data uncertainties and model uncertainties issues in computational mechanics. If data uncertainties can be modeled by parametric…”
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A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics by Soize, C.

“…In structural dynamics, a predictive model is constructed by developing a mathematical–mechanical model of a designed system in order to predict the response…”
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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 by Soize, C.

“…This paper is devoted to the identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields…”
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Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm by Soize, C.

“…This paper deals with the optimal design of a titanium mesoscale implant in a cortical bone for which the apparent elasticity tensor is modeled by a…”
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Computational nonlinear stochastic homogenization using a nonconcurrent multiscale approach for hyperelastic heterogeneous microstructures analysis by Clément, A., Soize, C., Yvonnet, J.

“…SUMMARY This paper is devoted to the computational nonlinear stochastic homogenization of a hyperelastic heterogeneous microstructure using a nonconcurrent…”
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A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension by Soize, C.

“…► Inverse method for identification of non-Gaussian tensor-valued random fields. ► Identification in high dimension of the posterior model using a new Bayes’…”
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Probabilistic learning for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics by Soize, C., Farhat, C.

“…Summary Recently, a novel nonparametric probabilistic method for modeling and quantifying model‐form uncertainties in nonlinear computational mechanics was…”
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A nonparametric probabilistic approach for quantifying uncertainties in low‐dimensional and high‐dimensional nonlinear models by Soize, C., Farhat, C.

“…Summary A nonparametric probabilistic approach for modeling uncertainties in projection‐based, nonlinear, reduced‐order models is presented. When experimental…”
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Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators by Soize, C.

“…This paper deals with the construction of a class of non-Gaussian positive-definite matrix-valued random fields whose mathematical properties allow elliptic…”
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Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials by Clément, A., Soize, C., Yvonnet, J.

“…► New stochastic non-concurrent multiscale approach for micro–meso–macro nonlinear mechanics. ► Polynomial chaos representation of hyperelastic potential at…”
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Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites by Staber, B., Guilleminot, J., Soize, C., Michopoulos, J., Iliopoulos, A.

“…In this paper, we investigate the construction and identification of a new random field model for representing the constitutive behavior of laminated…”
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Polynomial chaos representation of databases on manifolds by Soize, C., Ghanem, R.

“…Characterizing the polynomial chaos expansion (PCE) of a vector-valued random variable with probability distribution concentrated on a manifold is a relevant…”
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A Novel Source-Filter Stochastic Model for Voice Production by Cataldo, E., Monteiro, L., Soize, C.

“…The novel stochastic model to produce voiced sounds proposed in this paper uses the source-filter Fant theory to generate voice signals and, consequently, it…”
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Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite by Le, T.T., Guilleminot, J., Soize, C.

“…This paper is concerned with the probabilistic multiscale analysis of polymeric materials reinforced by nanoscopic fillers. More precisely, this work is…”
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Identification and sampling of Bayesian posteriors of high-dimensional symmetric positive-definite matrices for data-driven updating of computational models by Arnst, M., Soize, C.

“…We propose a probabilistic methodology for data-driven updating of non-Gaussian high-dimensional symmetric positive-definite matrices involved in computational…”
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Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges by Soize, C.

“…•Novel methodology for the identification of stochastic models of uncertainties.•Linear computational structural dynamics for the low-and medium-frequency…”
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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 by Ezvan, O., Soize, C., Desceliers, C., Ghanem, R.

“…The paper presents an appropriate and efficient methodology for updating the control parameters of very large uncertain computational models, which are used…”
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Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures by Capillon, R., Desceliers, C., Soize, C.

“…This paper deals with the analysis of a stochastic reduced-order computational model in computational linear dynamics for linear viscoelastic composite…”
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Stochastic mechanical model of vocal folds for producing jitter and for identifying pathologies through real voices by Cataldo, E., Soize, C.

“…Jitter, in voice production applications, is a random phenomenon characterized by the deviation of the glottal cycle length with respect to a mean value. Its…”
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