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 85 articles , 1 standard article )

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  1. Zhang, Hong; Constantinescu, Emil M.; Smith, Barry F.: \textttPETScTSAdjoint: a discrete adjoint ODE solver for first-order and second-order sensitivity analysis (2022)
  2. Britton, Jolene; Chow, Yat Tin; Chen, Weitao; Xing, Yulong: Recovery of a time-dependent bottom topography function from the shallow water equations via an adjoint approach (2021)
  3. Fleischli, Benno; Mangani, Luca; Del Rio, Armando; Casartelli, Ernesto: A discrete adjoint method for pressure-based algorithms (2021)
  4. Panda, Nishant; Fernández-Godino, M. Giselle; Godinez, Humberto C.; Dawson, Clint: A data-driven non-linear assimilation framework with neural networks (2021)
  5. Akbarzadeh, Siamak; Hückelheim, Jan; Müller, Jens-Dominik: Consistent treatment of incompletely converged iterative linear solvers in reverse-mode algorithmic differentiation (2020)
  6. Mohanamuraly, P.; Hascoët, L.; Müller, J.-D.: Seeding and adjoining zero-halo partitioned parallel scientific codes (2020)
  7. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  8. Sgura, Ivonne; Lawless, Amos S.; Bozzini, Benedetto: Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation (2019)
  9. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  10. Dilgen, Cetin B.; Dilgen, Sumer B.; Fuhrman, David R.; Sigmund, Ole; Lazarov, Boyan S.: Topology optimization of turbulent flows (2018)
  11. 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)
  12. Stück, Arthur: Dual-consistency study for Green-Gauss gradient schemes in an unstructured Navier-Stokes method (2017)
  13. Tranquilli, Paul; Glandon, S. Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  14. Liao, Wenyuan: An adjoint-based Jacobi-type iterative method for elastic full waveform inversion problem (2015)
  15. Naumann, Uwe; Lotz, Johannes; Leppkes, Klaus; Towara, Markus: Algorithmic differentiation of numerical methods: tangent and adjoint solvers for parameterized systems of nonlinear equations (2015)
  16. 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)
  17. Stück, Arthur: An adjoint view on flux consistency and strong wall boundary conditions to the Navier-Stokes equations (2015)
  18. Cioaca, Alexandru; Sandu, Adrian: Low-rank approximations for computing observation impact in 4D-Var data assimilation (2014)
  19. Cioaca, Alexandru; Sandu, Adrian: An optimization framework to improve 4D-Var data assimilation system performance (2014)
  20. Hogan, Robin J.: Fast reverse-mode automatic differentiation using expression templates in C++ (2014)

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