References in zbMATH (referenced in 138 articles , 1 standard article )

Showing results 1 to 20 of 138.
Sorted by year (citations)

1 2 3 ... 5 6 7 next

  1. Büsing, Henrik: Efficient solution techniques for two-phase flow in heterogeneous porous media using exact Jacobians (2021)
  2. Larnier, K.; Monnier, J.; Garambois, P.-A.; Verley, J.: River discharge and bathymetry estimation from SWOT altimetry measurements (2021)
  3. Akbarzadeh, Siamak; Hückelheim, Jan; Müller, Jens-Dominik: Consistent treatment of incompletely converged iterative linear solvers in reverse-mode algorithmic differentiation (2020)
  4. Banović, Mladen; Vasilopoulos, Ilias; Walther, Andrea; Meyer, Marcus: Algorithmic differentiation of an industrial airfoil design tool coupled with the adjoint CFD method (2020)
  5. 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)
  6. Chandramouli, Pranav; Memin, Etienne; Heitz, Dominique: 4D large scale variational data assimilation of a turbulent flow with a dynamics error model (2020)
  7. 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)
  8. Ferrero, Andrea; Iollo, Angelo; Larocca, Francesco: Field inversion for data-augmented RANS modelling in turbomachinery flows (2020)
  9. Laue, Sören; Mitterreiter, Matthias; Giesen, Joachim: A simple and efficient tensor calculus for machine learning (2020)
  10. Mohanamuraly, P.; Hascoët, L.; Müller, J.-D.: Seeding and adjoining zero-halo partitioned parallel scientific codes (2020)
  11. Niewiarowski, Alexander; Adriaenssens, Sigrid; Pauletti, Ruy Marcelo: Adjoint optimization of pressurized membrane structures using automatic differentiation tools (2020)
  12. Peñuñuri, F.; Peón, R.; González-Sánchez, D.; Escalante Soberanis, M. A.: Dual numbers and automatic differentiation to efficiently compute velocities and accelerations (2020)
  13. Fraysse, François; Saurel, Richard: Automatic differentiation using operator overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models (2019)
  14. 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)
  15. Griewank, Andreas; Walther, Andrea: Finite convergence of an active signature method to local minima of piecewise linear functions (2019)
  16. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  17. Naumann, Uwe: Adjoint code design patterns (2019)
  18. 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)
  19. 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)
  20. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)

1 2 3 ... 5 6 7 next


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