Jan A. Vierendeels

Computational Mechanics · 2007–2010

A researcher in the Science corpus: 7 papers; trusts 33 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

Whom Jan A. Vierendeels trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Thuan Lieu
  2. Charbel Farhat
  3. Nicholas John Higham
  4. Hermann G. Matthies
  5. Joris Degroote
  6. Klaus-Jürgen Bathe
  7. Michael Hinze
  8. Martin A. Grepl
  9. Athanasios C. Antoulas
  10. Tan Bui-Thanh

Who trusts Jan A. Vierendeels overall

  1. Joris Degroote
  2. H. Baek
  3. Marco Fossati
  4. Benjamin Peherstorfer
  5. Christian Soize
  6. Constantin Grigo
  7. R. Haelterman
  8. Peter Bruggeman
  9. G. Em Karniadakis
  10. Klaus-Jürgen Bathe

Papers from Jan A. Vierendeels's perspective

Ordered by how strongly the researchers Jan A. Vierendeels trusts endorse each paper. Same corpus, a different order for every researcher.

  1. An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations
  2. Proper Orthogonal Decomposition Surrogate Models for Nonlinear Dynamical Systems: Error Estimates and Suboptimal Control
  3. EFFICIENT REDUCED-BASIS TREATMENT OF NONAFFINE AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS
  4. Matrix Computations
  5. Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space (SIAM Journal on Scientific Computing)
  6. Modeling and control of physical processes using proper orthogonal decomposition
  7. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows
  8. Added-mass effect in the design of partitioned algorithms for fluid-structure problems
  9. A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices (IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281))
  10. A Survey of Model Reduction Methods for Large-Scale Systems

Their papers