TAPENADE 2.1 user's guide

By Laurent Hascoët, Valérie Pascual. 2004

352 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/984577f3bd9a5c74ec554c91eb467b3c5f1416e6

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Citations in the corpus

Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013, methodology): “For C/C++, the software packages ADIC [15], OpenAD [93], and Tapenade [31] use the source-code transformation approach, while ADOL-C [30] and FADBAD++ [10] use the operator-loading approach.”
TMB: Automatic Differentiation and Laplace Approximation (2015, methodology): “, the package TAPENADE (Hascoet and Pascual 2004), a preprocessor generates derivative code that is compiled together with the original program.”
Lagrangian data assimilation for river hydraulics simulations (2006, unknown): “Here, the direct program is coded in Fortran 90 and we use the automatic diﬀerentiation tool Tapenade [10].”
Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods (2009, methodology): “The TAPENADE automatic differentiation engine (Hasco¨et and Pascual, 2004), a source-to-source transformation program, was adopted because of its ﬂexibility and efﬁciency for both forward and reverse modes.”
Assimilation of spatially distributed water levels into a shallow-water flood model. Part I: Mathematical method and test case (2009, unknown): “The code differentiation is done using the automatic differentiation tool Tapenade, [10].”
Using Automatic Differentiation to Create a Nonlinear Reduced-Order-Model Aerodynamic Solver (2010, methodology): “In Thomas et al. [13], we originally used an automatic differentiation tool known as TAPENADE [15] to derive the Jacobians and tensors in Eq.”
The Tapenade automatic differentiation tool: Principles, model, and specification (2013, methodology): “Rather than a plain presentation of Tapenade describing its Java implementation, the available tool options and other ways to interact with it, which may change over time and is more the role of a user manual [12], this paper is meant to specify and formalize the AD model that was chosen for Tapenad”
Heliostat field optimization: A new computationally efficient model and biomimetic layout (2012, unknown): “This results in two main requirements, namely (i) computationally efficient calculation of efficiency with high accuracy and (ii) suitability for differentiation using algorithmic differentiation (AD) tools [Griewank and Walther(2008), Hascoët and Pascual(2004)].”