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[1-10]
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[Title],
[Author],
[Editor],
[Year] |
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Daniele Casanova, Robin S. Sharp, Mark Final, Bruce Christianson, Pat Symonds
Application of Automatic Differentiation to Race Car Performance Optimisation
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
not yet classified
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David E. Keyes, Paul D. Hovland, Lois C. McInnes, Widodo Samyono
Using Automatic Differentiation for Second-order Matrix-free Methods in PDE-Constrained Optimization
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
not yet classified
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Edgar J. Soulié, Christèle Faure, Théo Berclaz, Michel Geoffroy
Electron Paramagnetic Resonance, Optimization and Automatic Differentiation
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
not yet classified
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Jason Abate, Steve Benson, Lisa Grignon, Paul D. Hovland, Lois C. McInnes, Boyana Norris
Integrating AD with Object-Oriented Toolkits for High-performance Scientific Computing
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
Theory & Techniques:
Toolkits
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Jean-Daniel Beley, Stephane Garreau, Frederic Thevenon, Mohamed Masmoudi
Application of Higher Order Derivatives to Parameterization
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
not yet classified
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Laurent Hascoët, Stefka Fidanova, Christophe Held
Adjoining Independent Computations
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
Application Area:
Computational Fluid Dynamics
Tools:
TAPENADE
Theory & Techniques:
Reverse Mode
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Mark S. Gockenbach, Daniel R. Reynolds, William W. Symes
Automatic Differentiation and the Adjoint State Method
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
Theory & Techniques:
Adjoint
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Mohamed Tadjouddine, Shaun A. Forth, John D. Pryce
AD Tools and Prospects for Optimal AD in CFD Flux Jacobian Calculations
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
Application Area:
Computational Fluid Dynamics
Tools:
AD01, ADIFOR, TAMC
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Shahadat Hossain, Trond Steihaug
Reducing the Number of AD Passes for Computing a Sparse Jacobian Matrix
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
Theory & Techniques:
Sparsity
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Yuri G. Evtushenko, E. S. Zasuhina, V. I. Zubov
FAD Method to Compute Second Order Derivatives
Automatic Differentiation of Algorithms: From Simulation to Optimization, Springer,
2002 |
Theory & Techniques:
Hessian
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[1-10]
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