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Order by: [Title], [Author], [Editor], [Year]
Jan C. Hückelheim, Paul D. Hovland, Michelle M. Strout, Jens-Dominik Müller
Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
Application Area:
Computational Fluid Dynamics
Tools:
TAPENADE
Theory & Techniques:
Adjoint, Code Optimization, control-flow reversal, Data Flow Analysis, data-flow reversal, Implementation Strategies, Parallelism, Performance, Reverse Mode, Source transformation
John D. Pryce, Nedialko S. Nedialkov, Guangning Tan, Xiao Li
How AD can help solve differential-algebraic equations
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
Olivier Mullier, Alexandre Chapoutot, dit Sandretto, Julien Alexandre
Validated computation of the local truncation error of Runge--Kutta methods with automatic differentiation
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
Richard D. Neidinger, Benjamin Altman
Comparing high-order multivariate AD methods
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified

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