References in zbMATH (referenced in 133 articles , 1 standard article )

Showing results 1 to 20 of 133.
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  1. Büsing, Henrik: Efficient solution techniques for two-phase flow in heterogeneous porous media using exact Jacobians (2021)
  2. Banović, Mladen; Vasilopoulos, Ilias; Walther, Andrea; Meyer, Marcus: Algorithmic differentiation of an industrial airfoil design tool coupled with the adjoint CFD method (2020)
  3. Casado, Jose Maria Varas; Hewson, Rob: Algorithm 1008: multicomplex number class for Matlab, with a focus on the accurate calculation of small imaginary terms for multicomplex step sensitivity calculations (2020)
  4. Chandramouli, Pranav; Memin, Etienne; Heitz, Dominique: 4D large scale variational data assimilation of a turbulent flow with a dynamics error model (2020)
  5. Ferreiro-Ferreiro, A. M.; García-Rodríguez, J. A.; López-Salas, J. G.; Escalante, C.; Castro, M. J.: Global optimization for data assimilation in landslide tsunami models (2020)
  6. Ferrero, Andrea; Iollo, Angelo; Larocca, Francesco: Field inversion for data-augmented RANS modelling in turbomachinery flows (2020)
  7. Mohanamuraly, P.; Hascoët, L.; Müller, J.-D.: Seeding and adjoining zero-halo partitioned parallel scientific codes (2020)
  8. Fraysse, François; Saurel, Richard: Automatic differentiation using operator overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models (2019)
  9. Gejadze, I.; Malaterre, P.-O.; Shutyaev, V.: On the use of derivatives in the polynomial chaos based global sensitivity and uncertainty analysis applied to the distributed parameter models (2019)
  10. Griewank, Andreas; Walther, Andrea: Finite convergence of an active signature method to local minima of piecewise linear functions (2019)
  11. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  12. Naumann, Uwe: Adjoint code design patterns (2019)
  13. Wang, Mengze; Wang, Qi; Zaki, Tamer A.: Discrete adjoint of fractional-step incompressible Navier-Stokes solver in curvilinear coordinates and application to data assimilation (2019)
  14. 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)
  15. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  16. 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)
  17. Cots, Olivier; Gergaud, Joseph; Goubinat, Damien: Direct and indirect methods in optimal control with state constraints and the climbing trajectory of an aircraft (2018)
  18. DeGroot, Christopher T: WEdiff: A Python and C++ package for automatic differentiation (2018) not zbMATH
  19. Dilgen, Cetin B.; Dilgen, Sumer B.; Fuhrman, David R.; Sigmund, Ole; Lazarov, Boyan S.: Topology optimization of turbulent flows (2018)
  20. Hascoët, Laurent; Morlighem, M.: Source-to-source adjoint algorithmic differentiation of an ice sheet model written in C (2018)

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