Applied mathematics, Uncertainty quantification, Mathematical analysis, Random element and Random field are his primary areas of study. His studies in Applied mathematics integrate themes in fields like Stochastic modelling, Random matrix, Stochastic process, Mathematical optimization and Calculus. His Uncertainty quantification research incorporates themes from Nonparametric statistics, Dynamical systems theory, Probabilistic logic, Statistical physics and Computational mechanics.
Christian Soize interconnects Monte Carlo method and Dissipative system in the investigation of issues within Mathematical analysis. His work deals with themes such as Stochastic simulation, Probability distribution, Random function and Multivariate random variable, which intersect with Random element. His work in Random field tackles topics such as Polynomial chaos which are related to areas like Inverse problem and Random variable.
Christian Soize mainly focuses on Uncertainty quantification, Applied mathematics, Probabilistic logic, Mathematical analysis and Nonlinear system. The various areas that he examines in his Uncertainty quantification study include Nonparametric statistics, Random matrix, Parametric statistics, Mathematical optimization and Statistical model. The various areas that Christian Soize examines in his Applied mathematics study include Random variable, Multivariate random variable, Principle of maximum entropy, Stochastic process and Random field.
His Probabilistic logic research is multidisciplinary, relying on both Algorithm, Optimization problem, Computational model and Dynamical systems theory. As part of one scientific family, Christian Soize deals mainly with the area of Mathematical analysis, narrowing it down to issues related to the Finite element method, and often Discretization. His biological study spans a wide range of topics, including Vibration, Mistuning and Mechanics.
Christian Soize mainly investigates Probabilistic logic, Uncertainty quantification, Applied mathematics, Nonlinear system and Algorithm. He interconnects Basis, Nonparametric statistics, Mathematical optimization, Computational model and Hyperparameter in the investigation of issues within Probabilistic logic. His studies in Uncertainty quantification integrate themes in fields like Random matrix, Mathematical analysis, Control theory, Reduction and Statistical physics.
In his articles, he combines various disciplines, including Applied mathematics and Context. His Nonlinear system research is multidisciplinary, incorporating perspectives in Mistuning, Vibration, Finite element method and Mechanics, Slosh dynamics. His Algorithm research also works with subjects such as
Christian Soize spends much of his time researching Probabilistic logic, Mathematical optimization, Uncertainty quantification, Applied mathematics and Nonlinear system. The concepts of his Probabilistic logic study are interwoven with issues in Probability distribution, Diffusion map, Small data, Optimization problem and Stiefel manifold. His research integrates issues of Nonparametric statistics, Probability density function and Multivariate random variable in his study of Mathematical optimization.
His research in Uncertainty quantification intersects with topics in Reduction, Model order reduction, Algorithm, Hyperparameter and Gaussian. His Applied mathematics study incorporates themes from Basis, Random variable, Principle of maximum entropy, Random field and Eigenvalues and eigenvectors. Christian Soize has researched Nonlinear system in several fields, including Vibration, Frequency band, Finite element method and Dissipative system.
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A nonparametric model of random uncertainties for reduced matrix models in structural dynamics
Probabilistic Engineering Mechanics (2000)
Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
Christian Soize;Roger Ghanem.
computational science and engineering (2005)
Structural Acoustics and Vibration: Mechanical Models, Variational Formulations and Discretization
Christian Soize;Roger Ohayon.
Maximum entropy approach for modeling random uncertainties in transient elastodynamics.
Journal of the Acoustical Society of America (2001)
Random matrix theory for modeling uncertainties in computational mechanics
Computer Methods in Applied Mechanics and Engineering (2005)
The Fokker-Planck Equation for Stochastic Dynamical Systems and Its Explicit Steady State Solutions
A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics
Journal of Sound and Vibration (2005)
Structural Acoustics and Vibration
Roger Ohayon;Christian Soize.
Journal of the Acoustical Society of America (2001)
A model and numerical method in the medium frequency range for vibroacoustic predictions using the theory of structural fuzzy
Journal of the Acoustical Society of America (1992)
Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators
Computer Methods in Applied Mechanics and Engineering (2006)
Profile was last updated on December 6th, 2021.
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