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 a Structural Dynamics Solver -- A Bibliography of Automatic Differentiation.

This collection covers the state of the art in automatic differentiation theory and practice. Practitioners and students will learn about advances in automatic differentiation techniques and strategies for the implementation of robust and powerful tools. Computational scientists and engineers will benefit from the discussion of applications, which provide insight into effective strategies for using automatic differentiation for design optimization, sensitivity analysis, and uncertainty quantification.