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Mu Wang, Guang Lin, Alex Pothen
Using automatic differentiation for compressive sensing in uncertainty quantification
Article in Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation, Taylor & Francis, 2018
not yet classified
H. Olsson, H. Tummescheit, H. Elmqvist
Using automatic differentiation for partial derivatives of functions in Modelica
Conference proceeding, Proceedings of Modelica2005,
Application Area:
Differential-Algebraic Equation, Dynamical Systems, Ordinary Differential Equations
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
H. M. Bücker, R. Beucker, C. H. Bischof
Using Automatic Differentiation for the Minimal p-Norm Solution of the Biomagnetic Inverse Problem
Conference proceeding, Shaping Future with Simulation, Proceedings of the 4th International Eurosim 2001 Congress, Delft, The Netherlands, June 26--29, 2001, Dutch Benelux Simulation Society, 2001
Application Area:
Biomedicine
Tools:
ADIFOR
H. M. Bücker, R. Beucker
Using automatic differentiation for the solution of the minimum p-norm estimation problem in magnetoencephalography
RWTH, Institute for Scientific Computing, 2002
Application Area:
Biomedicine
Tools:
TAF
Theory & Techniques:
Adjoint
H. M. Bücker, R. Beucker
Using automatic differentiation for the solution of the minimum p-norm estimation problem in magnetoencephalography
Article in Simulation Modelling Practice and Theory, 2004
Application Area:
Biomedicine
Tools:
TAF
Mihai Alexe, Oleg Roderick, Mihai Anitescu, Jean Utke, Thomas Fanning, Paul Hovland.
Using Automatic Differentiation in Sensitivity Analysis of Nuclear Simulation Models
Article in Transactions of the American Nuclear Society, 2010
Application Area:
Engineering
Tools:
OpenAD, TAPENADE
Theory & Techniques:
Forward Mode, Reverse Mode
R. Steiger, C. H. Bischof, B. Lang, W. Thiel
Using Automatic Differentiation to Compute Derivatives for a Quantum-Chemical Computer Program
Article in Future Generation Computer Systems, 2005
Application Area:
Chemistry
Tools:
ADIFOR
Claire Lauvernet, Laurent Hascoët, François-Xavier Le Dimet, Frédéric Baret
Using Automatic Differentiation to Study the Sensitivity of a Crop Model
Recent Advances in Algorithmic Differentiation, Springer, 2012
Tools:
TAPENADE
Thomas F. Coleman, Xin Xiong, Wei Xu
Using Directed Edge Separators to Increase Efficiency in the Determination of Jacobian Matrices via Automatic Differentiation
Recent Advances in Algorithmic Differentiation, Springer, 2012
not yet classified
Henrik Büsing, Johannes Willkomm, Christian H. Bischof, Christoph Clauser
Using Exact Jacobians in an Implicit Newton Method for Solving Multiphase Flow in Porous Media
Article in International Journal of Computational Science and Engineering, 2014
Application Area:
Geophysics
Tools:
ADiMat
F. D. Bramkamp, H. M. Bücker, A. Rasch
Using Exact Jacobians in an Implicit Newton-Krylov Method
Article in Computers & Fluids, 2006
Application Area:
Computational Fluid Dynamics
Tools:
ADIFOR
Michael C. Bartholomew-Biggs
Using Forward Accumulation for Automatic Differentiation of Implicitly-Defined Functions
Article in Computational Optimization and Applications, 1998
not yet classified
P. Brown, A. C. Hindmarsh, L. R. Petzold
Using Krylov Methods in the Solution of Large-Scale Differential-Algebraic Systems
Article in SIAM Journal on Scientific Computing, 1994
not yet classified
Gregory Lantoine, Ryan P. Russell, Thierry Dargent
Using Multicomplex Variables for Automatic Computation of High-Order Derivatives
Article in ACM Trans. Math. Softw., ACM, 2012
Theory & Techniques:
Multicomplex Step Differentiation
Jeffrey Mark Siskind, Barak A. Pearlmutter
Using Polyvariant Union-Free Flow Analysis to Compile a Higher-Order Functional-Programming Language with a First-Class Derivative Operator to Efficient Fortran-like Code
School of Electrical and Computer Engineering, Purdue University, 2008
Theory & Techniques:
Forward Mode, Functional Programming, Higher Order, Nesting
Barak A. Pearlmutter, Jeffrey Mark Siskind
Using Programming Language Theory to Make Automatic Differentiation Sound and Efficient
Advances in Automatic Differentiation, Springer, 2008
Theory & Techniques:
Functional Programming
R. Giering, T. Kaminski
Using TAMC to generate efficient adjoint code: Comparison of automatically generated code for evaluation of first and second order derivatives to hand written code from the Minpack-2 collection
Automatic Differentiation for Adjoint Code Generation, INRIA, 1998
Application Area:
General
Tools:
TAMC
Theory & Techniques:
Hessian, Performance
Jan Riehme, Uwe Naumann
Using the Differentiation-Enabled NAGWare Fortran 95 Compiler -- A Guided Tour
Conference proceeding, ECCOMAS 2004: Fourth European Congress on Computational Methods in Applied Sciences and Engineering, European Community on Computational Methods in Applied Sciences, 2004
Tools:
NAGWare Fortran 95
D. Elizondo, B. Cappelaere, C. Faure
Using the Odyssée automatic differentiation tool for inverse modeling
24th General Assembly of the EGS - Hydrology, Oceans and Atmosphere, The Hague, April 1999, 1999
Application Area:
Geophysics
Tools:
Odyssee

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