Tan Bui-Thanh
Statistics and Probability · 2003–2013
A researcher in the Science corpus: 4 papers; trusts 20 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 15 citations into it; #1,932 of 12,932 by trust here, #2,751 by citations
- Statistical and Nonlinear Physics — 12 citations into it; #940 of 10,127 by trust here, #2,158 by citations
- Computational Mechanics — 11 citations into it; #2,915 of 10,001 by trust here, #4,937 by citations
- Artificial Intelligence — 8 citations into it
Whom Tan Bui-Thanh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karen Veroy
- Martin A. Grepl
- Håvard Rue
- Anthony T. Patera
- Yvon Maday
- Albert Tarantola
- Michael Hinze
- Christophe Prud'Homme
- Gianluigi Rozza
- Dimitrios Rovas
Who trusts Tan Bui-Thanh overall
- Karen E. Willcox
- Saifon Chaturantabut
- Kevin T. Carlberg
- Danny C. Sorensen
- L. Mainini
- Benjamin Peherstorfer
- Charbel Farhat
- Jincong He
- Gianluigi Rozza
- Peter Benner
Papers from Tan Bui-Thanh's perspective
Ordered by how strongly the researchers Tan Bui-Thanh trusts endorse each paper. Same corpus, a different order for every researcher.
- An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations
- Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds
- A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations
- Gaussian Markov Random Fields: Theory and Applications
- The finite element method for elliptic problems (Classics in applied mathematics)
- Riemann manifold Langevin and Hamiltonian Monte Carlo methods
- A blackbox reduced-basis output bound method for noncoercive linear problems
- A Posteriori Error Bounds for Reduced-Basis Approximation of Parametrized Noncoercive and Nonlinear Elliptic Partial Differential Equations
- Balanced Model Reduction via the Proper Orthogonal Decomposition
- Modeling and control of physical processes using proper orthogonal decomposition
Their papers
- Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space (2008) — 418 citations
- Aerodynamic Data Reconstruction and Inverse Design Using Proper Orthogonal Decomposition (2004) — 542 citations
- A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion (2013) — 439 citations
- Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics (2003) — 265 citations