TAF

AD Tool: TAF Transformation of Algorithms in Fortran (TAF) is a source-to-source AD-tool for Fortran-95 programs. TAF supports forward and reverse mode of AD and Automatic Sparsity Detection (ASD) for detection of the sparsity structure of Jacobians. TAF normalizes the code and applies a control flow analysis. TAF applies an intraprocedural data dependence and an interprocedural data flow analysis. Given the independent and dependent variables of the specified top-level routine, TAF determines all active routines and variables and produces derivative code only for those.


References in zbMATH (referenced in 69 articles , 1 standard article )

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  1. Tranquilli, Paul; Glandon, S.Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  2. Naumann, Uwe; Lotz, Johannes; Leppkes, Klaus; Towara, Markus: Algorithmic differentiation of numerical methods: tangent and adjoint solvers for parameterized systems of nonlinear equations (2015)
  3. Reilly, Jack; Samaranayake, Samitha; Delle Monache, Maria Laura; Krichene, Walid; Goatin, Paola; Bayen, Alexandre M.: Adjoint-based optimization on a network of discretized scalar conservation laws with applications to coordinated ramp metering (2015)
  4. Stück, Arthur: An adjoint view on flux consistency and strong wall boundary conditions to the Navier-Stokes equations (2015)
  5. Cioaca, Alexandru; Sandu, Adrian: Low-rank approximations for computing observation impact in 4D-Var data assimilation (2014)
  6. Cioaca, Alexandru; Sandu, Adrian: An optimization framework to improve 4D-Var data assimilation system performance (2014)
  7. Hogan, Robin J.: Fast reverse-mode automatic differentiation using expression templates in C++ (2014)
  8. Losch, Martin; Fuchs, Annika; Lemieux, Jean-François; Vanselow, Anna: A parallel Jacobian-free Newton-Krylov solver for a coupled sea ice-ocean model (2014)
  9. Gratton, Serge; Gürol, Selime; Toint, Philippe L.: Preconditioning and globalizing conjugate gradients in dual space for quadratically penalized nonlinear-least squares problems (2013)
  10. Stück, Arthur; Rung, Thomas: Adjoint complement to viscous finite-volume pressure-correction methods (2013)
  11. Chen, Haibo; Miao, Chunbao; Lv, Xianqing: A three-dimensional numerical internal tidal model involving adjoint method (2012)
  12. Cole-Mullen, Heather; Lyons, Andrew; Utke, Jean: Storing versus recomputation on multiple DAGs (2012)
  13. Gauger, Nicolas; Walther, Andrea; Özkaya, Emre; Moldenhauer, Carsten: Efficient aerodynamic shape optimization by structure exploitation (2012)
  14. Hossen, M.J.; Navon, I.M.; Daescu, D.N.: Effect of random perturbations on adaptive observation techniques (2012)
  15. Solonen, Antti; Haario, Heikki; Hakkarainen, Janne; Auvinen, Harri; Amour, Idrissa; Kauranne, Tuomo: Variational ensemble Kalman filtering using limited memory BFGS (2012)
  16. Wu, Lin; Le Dimet, François-Xavier; de Reffye, Philippe; Hu, Bao-Gang; Cournède, Paul-Henry; Kang, Meng-Zhen: An optimal control methodology for plant growth-case study of a water supply problem of sunflower (2012)
  17. Bücker, H.Martin; Slusanschi, Emil: Second-order derivatives of the general-purpose finite element package SEPRAN via source transformation (2011)
  18. Christakopoulos, Faidon; Jones, Dominic; Müller, Jens-Dominik: Pseudo-timestepping and verification for automatic differentiation derived CFD codes (2011)
  19. Espath, L.F.R.; Linn, R.V.; Awruch, A.M.: Shape optimization of shell structures based on NURBS description using automatic differentiation (2011)
  20. Godinez, Humberto C.; Daescu, Dacian N.: Observation targeting with a second-order adjoint method for increased predictability (2011)

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