David J. Knezevic

Computational Mechanics · 2010–2013

A researcher in the Science corpus: 4 papers; trusts 14 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

Whom David J. Knezevic trusts overall

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

  1. Anthony T. Patera
  2. Gianluigi Rozza
  3. Dinh Bao Phuong Huynh
  4. Martin A. Grepl
  5. Karen Veroy
  6. Ngoc Cuong Nguyen
  7. Bernard Haasdonk
  8. Alfio Maria Quarteroni
  9. Christophe Prud'Homme
  10. Yvon Maday

Who trusts David J. Knezevic overall

  1. Andrea Manzoni
  2. Alfio Maria Quarteroni
  3. Toni Lassila
  4. Gianluigi Rozza
  5. Christophe Audouze
  6. Jens L. Eftang
  7. Bernard Haasdonk
  8. Martin A. Grepl
  9. Saifon Chaturantabut
  10. F. De Vuyst

Papers from David J. Knezevic's perspective

Ordered by how strongly the researchers David J. Knezevic 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. Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds
  3. EFFICIENT REDUCED-BASIS TREATMENT OF NONAFFINE AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS
  4. Reliable Real-Time Solution of Parametrized Partial Differential Equations: Reduced-Basis Output Bound Methods
  5. Reduced-basis approximation of the viscous Burgers equation: rigorous a posteriori error bounds
  6. Certified Real-Time Solution of Parametrized Partial Differential Equations
  7. Reduced basis approximation and a posteriori error estimation for the time-dependent viscous Burgers’ equation
  8. Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity
  9. POD and CVT-based reduced-order modeling of Navier-Stokes flows
  10. A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations

Their papers