References in zbMATH (referenced in 120 articles , 1 standard article )

Showing results 1 to 20 of 120.
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  1. Ferrero, Andrea; Iollo, Angelo; Larocca, Francesco: Field inversion for data-augmented RANS modelling in turbomachinery flows (2020)
  2. Griewank, Andreas; Walther, Andrea: Finite convergence of an active signature method to local minima of piecewise linear functions (2019)
  3. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  4. Naumann, Uwe: Adjoint code design patterns (2019)
  5. Banović, Mladen; Mykhaskiv, Orest; Auriemma, Salvatore; Walther, Andrea; Legrand, Herve; Müller, Jens-Dominik: Algorithmic differentiation of the Open CASCADE technology CAD kernel and its coupling with an adjoint CFD solver (2018)
  6. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  7. Bissuel, Aloïs; Allaire, Grégoire; Daumas, Laurent; Barré, Sébastien; Rey, Floriane: Linearized Navier-Stokes equations for aeroacoustics using stabilized finite elements: boundary conditions and industrial application to aft-Fan noise propagation (2018)
  8. Cots, Olivier; Gergaud, Joseph; Goubinat, Damien: Direct and indirect methods in optimal control with state constraints and the climbing trajectory of an aircraft (2018)
  9. DeGroot, Christopher T: WEdiff: A Python and C++ package for automatic differentiation (2018) not zbMATH
  10. Dilgen, Cetin B.; Dilgen, Sumer B.; Fuhrman, David R.; Sigmund, Ole; Lazarov, Boyan S.: Topology optimization of turbulent flows (2018)
  11. Hascoët, Laurent; Morlighem, M.: Source-to-source adjoint algorithmic differentiation of an ice sheet model written in C (2018)
  12. Jiang, Ting; Zhou, XiaoJian: Gradient/Hessian-enhanced least square support vector regression (2018)
  13. Pascual, Valérie; Hascoët, Laurent: Mixed-language automatic differentiation (2018)
  14. Rodrigues, S. S.; Marta, A. C.: Adjoint formulation of a steady multistage turbomachinery interface using automatic differentiation (2018)
  15. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  16. Shi-Dong, Doug; Nadarajah, Siva: Approximate Hessian for accelerated convergence of aerodynamic shape optimization problems in an adjoint-based framework (2018)
  17. Siskind, Jeffrey Mark; Pearlmutter, Barak A.: Divide-and-conquer checkpointing for arbitrary programs with no user annotation (2018)
  18. Srajer, Filip; Kukelova, Zuzana; Fitzgibbon, Andrew: A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning (2018)
  19. Towara, M.; Naumann, U.: SIMPLE adjoint message passing (2018)
  20. Zhou, Beckett Y.; Ryong Koh, Seong; Gauger, Nicolas R.; Meinke, Matthias; Schöder, Wolfgang: A discrete adjoint framework for trailing-edge noise minimization via porous material (2018)

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Further publications can be found at: http://www-sop.inria.fr/tropics/tapenade.html