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- Dongbin Xiu

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
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Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
34
Citations
9,235
79
World Ranking
3799
National Ranking
1447

- Mathematical analysis
- Statistics
- Algebra

Dongbin Xiu focuses on Mathematical optimization, Polynomial chaos, Stochastic process, Applied mathematics and Galerkin method. His work carried out in the field of Mathematical optimization brings together such families of science as Collocation method, Sampling, Ensemble Kalman filter, Gaussian noise and Collocation. His Polynomial chaos research is multidisciplinary, incorporating perspectives in Projection, Extended Kalman filter, Linear system and Orthogonal polynomials.

In most of his Stochastic process studies, his work intersects topics such as Mathematical analysis. His Applied mathematics research is multidisciplinary, incorporating elements of Uncertainty quantification, Polynomial and Stochastic optimization. The various areas that Dongbin Xiu examines in his Stochastic optimization study include Stochastic differential equation, Stochastic partial differential equation and Spectral method.

- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (3207 citations)
- High-Order Collocation Methods for Differential Equations with Random Inputs (1213 citations)
- Modeling uncertainty in flow simulations via generalized polynomial chaos (1038 citations)

Dongbin Xiu mainly focuses on Mathematical optimization, Applied mathematics, Uncertainty quantification, Algorithm and Mathematical analysis. Dongbin Xiu has researched Mathematical optimization in several fields, including Uncertainty analysis, Computation, Collocation method and Collocation. His Applied mathematics research incorporates themes from Numerical analysis, Collocation, Galerkin method and Ordinary differential equation.

His Uncertainty quantification study combines topics from a wide range of disciplines, such as Stochastic galerkin, Monte Carlo method and Random variable. Mathematical analysis is closely attributed to Polynomial chaos in his research. In his work, Projection, Orthogonal polynomials, Field and Sensitivity is strongly intertwined with Stochastic process, which is a subfield of Polynomial chaos.

- Mathematical optimization (35.71%)
- Applied mathematics (31.75%)
- Uncertainty quantification (27.78%)

- Artificial neural network (11.11%)
- Algorithm (19.84%)
- Applied mathematics (31.75%)

The scientist’s investigation covers issues in Artificial neural network, Algorithm, Applied mathematics, Dynamical systems theory and Artificial intelligence. His work deals with themes such as Uncertainty quantification, Structure, Stochastic geometry and Parameter space, which intersect with Algorithm. His research in Uncertainty quantification intersects with topics in Flow, Smoothness and Computation.

His Applied mathematics research integrates issues from Space, Numerical analysis, Partial differential equation and Galerkin method. His research integrates issues of Discretization and Wave equation in his study of Galerkin method. Within one scientific family, Dongbin Xiu focuses on topics pertaining to Parameterized complexity under Dynamical systems theory, and may sometimes address concerns connected to Uncertainty analysis, Piecewise, Polynomial regression, LTI system theory and Network model.

- Data driven governing equations approximation using deep neural networks (81 citations)
- Numerical Aspects for Approximating Governing Equations Using Data (37 citations)
- Data-driven deep learning of partial differential equations in modal space (33 citations)

- Statistics
- Mathematical analysis
- Algebra

His primary areas of study are Algorithm, Artificial neural network, Residual, Applied mathematics and Structure. His Uncertainty quantification research extends to Algorithm, which is thematically connected. His studies in Uncertainty quantification integrate themes in fields like Flow, Smoothness and Stochastic geometry.

His work carried out in the field of Artificial neural network brings together such families of science as Dynamical systems theory, Deep learning and Chaotic. He has included themes like Partial differential equation, Operator, Frequency domain, Inviscid flow and Space in his Residual study. In his study, he carries out multidisciplinary Applied mathematics and Trajectory research.

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.

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

Dongbin Xiu;George Em Karniadakis.

SIAM Journal on Scientific Computing **(2002)**

4210 Citations

Numerical Methods for Stochastic Computations: A Spectral Method Approach

Dongbin Xiu.

**(2010)**

1745 Citations

High-Order Collocation Methods for Differential Equations with Random Inputs

Dongbin Xiu;Jan S. Hesthaven.

SIAM Journal on Scientific Computing **(2005)**

1717 Citations

Modeling uncertainty in flow simulations via generalized polynomial chaos

Dongbin Xiu;George Em Karniadakis.

Journal of Computational Physics **(2003)**

1400 Citations

Fast numerical methods for stochastic computations: A review

Dongbin Xiu.

Communications in Computational Physics **(2009)**

739 Citations

Modeling Uncertainty in Steady State Diffusion Problems via Generalized Polynomial Chaos

Dongbin Xiu;George Em Karniadakis.

Computer Methods in Applied Mechanics and Engineering **(2002)**

644 Citations

Efficient collocational approach for parametric uncertainty analysis

Dongbin Xiu.

Communications in Computational Physics **(2007)**

500 Citations

Stochastic Modeling of Flow-Structure Interactions Using Generalized Polynomial Chaos

Dongbin Xiu;Didier Lucor;C.-H. Su;George Em Karniadakis.

Journal of Fluids Engineering-transactions of The Asme **(2002)**

343 Citations

A stochastic collocation approach to Bayesian inference in inverse problems

Youssef Marzouk;Dongbin Xiu.

Communications in Computational Physics **(2009)**

289 Citations

A new stochastic approach to transient heat conduction modeling with uncertainty

Dongbin Xiu;George Em Karniadakis.

International Journal of Heat and Mass Transfer **(2003)**

261 Citations

Brown University

Johns Hopkins University

University of Wisconsin–Madison

University of Utah

Purdue University West Lafayette

Imperial College London

Stanford University

University of Utah

Brown University

University of Notre Dame

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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