Titlebook: Recent Advances in Algorithmic Differentiation; Shaun Forth,Paul Hovland,Andrea Walther Conference proceedings 2012 Springer-Verlag Berlin

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A Leibniz Notation for Automatic Differentiation,entiation community is resolutely Newtonian. In this paper we extend the Leibniz notation to include the reverse (or adjoint) mode of Automatic Differentiation, and use it to demonstrate the stepwise numerical equivalence of the three approaches using the reverse mode to obtain second order derivati

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Combining Automatic Differentiation Methods for High-Dimensional Nonlinear Models,odels when the effective rank of the first-order sensitivity matrix is orders of magnitude smaller than the size of the input data. Here, the method is extended to handle nonlinear models, where the evaluation of higher-order derivatives is important but also challenging because the number of deriva

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Application of Automatic Differentiation to an Incompressible URANS Solver, incompressible flows. This adjoint solver is to be employed in active flow control problems to enhance the performance of aerodynamic configurations. We discuss on how the development of such a code can be eased through the use of the reverse mode of Automatic/Algorithmic Differentiation (AD). If A

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Applying Automatic Differentiation to the Community Land Model,tainty exists in model-based predictions. Evaluation and improvement of model codes are one of the priorities of climate science research. Automatic Differentiation enables analysis of sensitivities of predicted outcomes to input parameters by calculating derivatives of modeled functions. The result

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Using Automatic Differentiation to Study the Sensitivity of a Crop Model,d Dynamics. In agronomy, the differentiation of crop models has never been performed since these models are more empirical than fully mecanistic, derived from equations. This study shows the feasibility of constructing the adjoint model of a crop model referent in the agronomic community (STICS) wit

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