Welcome to the ADIC Resource Center. ADIC is a tool for the automatic differentiation (AD) of programs written in ANSI C. Given the source code and a user’s specification of dependent and independent variables, ADIC generates an augmented C code that computes the partial derivatives of all of the specified dependent variables with respect to all of the specified independent variables in addition to the original result. The purpose of this web is to provide the support services for users of ADIC software. Also, we are developing an ADIC Network Server where you will be able to submit your code and have it differentiated by the server and retrieve the differentiated code.

References in zbMATH (referenced in 83 articles , 1 standard article )

Showing results 1 to 20 of 83.
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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. Dolgakov, I.; Pavlov, D.: Landau: a language for dynamical systems with automatic differentiation (2020)
  3. Laue, Sören; Mitterreiter, Matthias; Giesen, Joachim: A simple and efficient tensor calculus for machine learning (2020)
  4. Dione, Ibrahima; Doyon, Nicolas; Deteix, Jean: Sensitivity analysis of the Poisson Nernst-Planck equations: a finite element approximation for the sensitive analysis of an electrodiffusion model (2019)
  5. Kharazmi, Ehsan; Zayernouri, Mohsen: Fractional sensitivity equation method: application to fractional model construction (2019)
  6. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  7. Charpentier, Isabelle; Gustedt, Jens: \textttArbogast: higher order automatic differentiation for special functions with Modular C (2018)
  8. 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)
  9. Kulshreshtha, K.; Narayanan, S. H. K.; Bessac, J.; MacIntyre, K.: Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C (2018)
  10. Kusch, Lisa; Albring, T.; Walther, A.; Gauger, N. R.: A one-shot optimization framework with additional equality constraints applied to multi-objective aerodynamic shape optimization (2018)
  11. Pascual, Valérie; Hascoët, Laurent: Mixed-language automatic differentiation (2018)
  12. Srajer, Filip; Kukelova, Zuzana; Fitzgibbon, Andrew: A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning (2018)
  13. Calver, Jonathan; Enright, Wayne: Numerical methods for computing sensitivities for ODEs and DDEs (2017)
  14. Coleman, Thomas F.; Xu, Wei: Automatic differentiation in MATLAB using ADMAT with applications (2016)
  15. Sluşanschi, Emil I.; Dumitrel, Vlad: ADiJaC -- automatic differentiation of Java classfiles (2016)
  16. Šolinc, Urša; Korelc, Jože: A simple way to improved formulation of (\textFE^2) analysis (2015)
  17. Callejo, A.; García de Jalón, J.: A hybrid direct-automatic differentiation method for the computation of independent sensitivities in multibody systems (2014)
  18. Goldsztejn, Alexandre; Cruz, Jorge; Carvalho, Elsa: Convergence analysis and adaptive strategy for the certified quadrature over a set defined by inequalities (2014)
  19. Zeng, X.; Anitescu, M.: Sequential Monte Carlo sampling in hidden Markov models of nonlinear dynamical systems (2014)
  20. Nehmeier, Marco: Interval arithmetic using expression templates, template meta programming and the upcoming C++ standard (2012)

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