- Home
- Best Scientists - Engineering and Technology
- Christian Soize

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
58
Citations
11,857
663
World Ranking
1194
National Ranking
18

- Quantum mechanics
- Statistics
- Mathematical analysis

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.

- Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure (405 citations)
- A nonparametric model of random uncertainties for reduced matrix models in structural dynamics (355 citations)
- Maximum entropy approach for modeling random uncertainties in transient elastodynamics. (260 citations)

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.

- Uncertainty quantification (28.62%)
- Applied mathematics (22.62%)
- Probabilistic logic (22.31%)

- Probabilistic logic (22.31%)
- Uncertainty quantification (28.62%)
- Applied mathematics (22.62%)

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

- Stochastic modelling, which have a strong connection to Jitter, Stochastic process, Probability density function and Inverse problem,
- Random field most often made with reference to Gaussian.

- Stochastic modeling and identification of a hyperelastic constitutive model for laminated composites (28 citations)
- A nonparametric probabilistic approach for quantifying uncertainties in low-dimensional and high-dimensional nonlinear models (19 citations)
- Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints (13 citations)

- Quantum mechanics
- Statistics
- Mathematical analysis

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure

Christian Soize;Roger Ghanem.

computational science and engineering **(2005)**

679 Citations

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

Christian Soize.

Probabilistic Engineering Mechanics **(2000)**

674 Citations

Structural Acoustics and Vibration: Mechanical Models, Variational Formulations and Discretization

Christian Soize;Roger Ohayon.

**(1997)**

517 Citations

Maximum entropy approach for modeling random uncertainties in transient elastodynamics.

Christian Soize.

Journal of the Acoustical Society of America **(2001)**

428 Citations

Random matrix theory for modeling uncertainties in computational mechanics

Christian Soize.

Computer Methods in Applied Mechanics and Engineering **(2005)**

373 Citations

The Fokker-Planck Equation for Stochastic Dynamical Systems and Its Explicit Steady State Solutions

Christian Soize.

**(1994)**

330 Citations

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

Christian Soize.

Journal of Sound and Vibration **(2005)**

310 Citations

Structural Acoustics and Vibration

Roger Ohayon;Christian Soize.

Journal of the Acoustical Society of America **(2001)**

278 Citations

A model and numerical method in the medium frequency range for vibroacoustic predictions using the theory of structural fuzzy

Christian Soize.

Journal of the Acoustical Society of America **(1992)**

231 Citations

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

T.G. Ritto;Christian Soize;R. Sampaio.

International Journal of Non-linear Mechanics **(2009)**

230 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

University of Southern California

Stanford University

Gustave Eiffel University

Sorbonne University

University of Innsbruck

Sandia National Laboratories

Rice University

University of Technology Sydney

École Polytechnique

École Polytechnique Fédérale de Lausanne

University of Virginia

Columbia University

United States Department of Agriculture

University of Western Ontario

MIT

University of Naples Federico II

Loughborough University

Soochow University

University of Montpellier

University of Edinburgh

University of Melbourne

Veterans Health Administration

Yale University

Aix-Marseille University

University of Southern California

National Institutes of Health

Something went wrong. Please try again later.