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 58 articles , 1 standard article )

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  1. 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)
  2. Gratton, Serge; Gürol, Selime; Toint, Philippe L.: Preconditioning and globalizing conjugate gradients in dual space for quadratically penalized nonlinear-least squares problems (2013)
  3. Cole-Mullen, Heather; Lyons, Andrew; Utke, Jean: Storing versus recomputation on multiple DAGs (2012)
  4. Gauger, Nicolas; Walther, Andrea; Özkaya, Emre; Moldenhauer, Carsten: Efficient aerodynamic shape optimization by structure exploitation (2012)
  5. Solonen, Antti; Haario, Heikki; Hakkarainen, Janne; Auvinen, Harri; Amour, Idrissa; Kauranne, Tuomo: Variational ensemble Kalman filtering using limited memory BFGS (2012)
  6. 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)
  7. Bücker, H.Martin; Slusanschi, Emil: Second-order derivatives of the general-purpose finite element package SEPRAN via source transformation (2011)
  8. Christakopoulos, Faidon; Jones, Dominic; Müller, Jens-Dominik: Pseudo-timestepping and verification for automatic differentiation derived CFD codes (2011)
  9. Espath, L.F.R.; Linn, R.V.; Awruch, A.M.: Shape optimization of shell structures based on NURBS description using automatic differentiation (2011)
  10. Godinez, Humberto C.; Daescu, Dacian N.: Observation targeting with a second-order adjoint method for increased predictability (2011)
  11. Jones, Dominic; Müller, Jens-Dominik; Christakopoulos, Faidon: Preparation and assembly of discrete adjoint CFD codes (2011)
  12. Stück, Arthur; Rung, Thomas: Adjoint RANS with filtered shape derivatives for hydrodynamic optimisation (2011)
  13. Wang, Yue-Peng; Tao, Su-Lin: Application of regularization technique to variational adjoint method: A case for nonlinear convection-diffusion problem (2011)
  14. Martinelli, M.; Duvigneau, R.: On the use of second-order derivatives and metamodel-based Monte Carlo method for uncertainty estimation in aerodynamics (2010)
  15. Rückelt, J.; Sauerland, V.; Slawig, T.; Srivastav, A.; Ward, B.; Patvardhan, C.: Parameter optimization and uncertainty analysis in a model of oceanic CO$_2$ uptake using a hybrid algorithm and algorithmic differentiation (2010)
  16. Stück, Arthur; Camelli, Fernando F.; Löhner, Rainald: Adjoint-based design of shock mitigation devices (2010)
  17. Zamani, Ahmadreza; Azimian, Ahmadreza; Heemink, Arnold; Solomatine, Dimitri: Non-linear wave data assimilation with an ANN-type wind-wave model and ensemble Kalman filter (EnKF) (2010)
  18. Alexe, Mihai; Sandu, Adrian: Forward and adjoint sensitivity analysis with continuous explicit Runge-Kutta schemes (2009)
  19. Alexe, Mihai; Sandu, Adrian: On the discrete adjoints of adaptive time stepping algorithms (2009)
  20. Cheng, Qiang; Cao, Jianwen; Wang, Bin; Zhang, Haibin: Adjoint code generator (2009)

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