Automatic Differentiation: Applications, Theory, and Implementations: Lecture Notes in Computational Science and Engineering, cartea 50
Editat de H. Martin Bücker, George Corliss, Paul Hovland, Uwe Naumann, Boyana Norrisen Limba Engleză Paperback – 14 dec 2005
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Specificații
ISBN-13: 9783540284031
ISBN-10: 3540284036
Pagini: 400
Ilustrații: XVIII, 370 p. 108 illus.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.54 kg
Ediția:2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540284036
Pagini: 400
Ilustrații: XVIII, 370 p. 108 illus.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.54 kg
Ediția:2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Perspectives on Automatic Differentiation: Past, Present, and Future?.- Backwards Differentiation in AD and Neural Nets: Past Links and New Opportunities.- Solutions of ODEs with Removable Singularities.- Automatic Propagation of Uncertainties.- High-Order Representation of Poincarée Maps.- Computation of Matrix Permanent with Automatic Differentiation.- Computing Sparse Jacobian Matrices Optimally.- Application of AD-based Quasi-Newton Methods to Stiff ODEs.- Reduction of Storage Requirement by Checkpointing for Time-Dependent Optimal Control Problems in ODEs.- Improving the Performance of the Vertex Elimination Algorithm for Derivative Calculation.- Flattening Basic Blocks.- The Adjoint Data-Flow Analyses: Formalization, Properties, and Applications.- Semiautomatic Differentiation for Efficient Gradient Computations.- Computing Adjoints with the NAGWare Fortran 95 Compiler.- Transforming Equation-Based Models in Process Engineering.- Extension of TAPENADE toward Fortran 95.- A Macro Language for Derivative Definition in ADiMat.- Simulation and Optimization of the Tevatron Accelerator.- Periodic Orbits of Hybrid Systems and Parameter Estimation via AD.- Implementation of Automatic Differentiation Tools for Multicriteria IMRT Optimization.- Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization.- Automatic Differentiation: A Tool for Variational Data Assimilation and Adjoint Sensitivity Analysis for Flood Modeling.- Development of an Adjoint for a Complex Atmospheric Model, the ARPS, using TAF.- Tangent Linear and Adjoint Versions of NASA/GMAO’s Fortran 90 Global Weather Forecast Model.- Efficient Sensitivities for the Spin-Up Phase.- Streamlined Circuit Device Model Development with fREEDAR® ãnd ADOL-C.- Adjoint Differentiation of aStructural Dynamics Solver.- A Bibliography of Automatic Differentiation.
Caracteristici
Includes supplementary material: sn.pub/extras