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 76 articles , 1 standard article )

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  1. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  2. Hück, Alexander; Bischof, Christian; Sagebaum, Max; Gauger, Nicolas R.; Jurgelucks, Benjamin; Larour, Eric; Perez, Gilberto: A usability case study of algorithmic differentiation tools on the ISSM ice sheet model (2018)
  3. Stück, Arthur: Dual-consistency study for Green-Gauss gradient schemes in an unstructured Navier-Stokes method (2017)
  4. Tranquilli, Paul; Glandon, S. Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  5. Liao, Wenyuan: An adjoint-based Jacobi-type iterative method for elastic full waveform inversion problem (2015)
  6. Naumann, Uwe; Lotz, Johannes; Leppkes, Klaus; Towara, Markus: Algorithmic differentiation of numerical methods: tangent and adjoint solvers for parameterized systems of nonlinear equations (2015)
  7. 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)
  8. Stück, Arthur: An adjoint view on flux consistency and strong wall boundary conditions to the Navier-Stokes equations (2015)
  9. Cioaca, Alexandru; Sandu, Adrian: Low-rank approximations for computing observation impact in 4D-Var data assimilation (2014)
  10. Cioaca, Alexandru; Sandu, Adrian: An optimization framework to improve 4D-Var data assimilation system performance (2014)
  11. Hogan, Robin J.: Fast reverse-mode automatic differentiation using expression templates in C++ (2014)
  12. Losch, Martin; Fuchs, Annika; Lemieux, Jean-François; Vanselow, Anna: A parallel Jacobian-free Newton-Krylov solver for a coupled sea ice-ocean model (2014)
  13. Maddison, J. R.; Farrell, P. E.: Rapid development and adjoining of transient finite element models (2014)
  14. Cioaca, Alexandru; Sandu, Adrian; de Sturler, Eric: Efficient methods for computing observation impact in 4D-Var data assimilation (2013)
  15. Fang, F.; Pain, C. C.; Navon, I. M.; Cacuci, D. G.; Chen, X.: The independent set perturbation method for efficient computation of sensitivities with applications to data assimilation and a finite element shallow water model (2013)
  16. Gratton, Serge; Gürol, Selime; Toint, Philippe L.: Preconditioning and globalizing conjugate gradients in dual space for quadratically penalized nonlinear-least squares problems (2013)
  17. Stück, Arthur; Rung, Thomas: Adjoint complement to viscous finite-volume pressure-correction methods (2013)
  18. Chen, Haibo; Miao, Chunbao; Lv, Xianqing: A three-dimensional numerical internal tidal model involving adjoint method (2012)
  19. Cole-Mullen, Heather; Lyons, Andrew; Utke, Jean: Storing versus recomputation on multiple DAGs (2012)
  20. Gauger, Nicolas; Walther, Andrea; Özkaya, Emre; Moldenhauer, Carsten: Efficient aerodynamic shape optimization by structure exploitation (2012)

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