A modular system of algorithms for unconstrained minimization
ACM Transactions on Mathematical Software (TOMS)
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
A conserving discretization for the free boundary in a two-dimensional Stefan problem
Journal of Computational Physics
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
IEEE Computational Science & Engineering
Computing in Science and Engineering
On the Use of a Differentiated Finite Element Package for Sensitivity Analysis
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
An interactive environment for supporting the transition from simulation to optimization
Scientific Programming - POOSC '01 Workshop
Sensitivities for a single drop simulation
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
EFCOSS: An interactive environment facilitating optimal experimental design
ACM Transactions on Mathematical Software (TOMS)
Hi-index | 0.00 |
Derivatives play a prominent role in many areas of scientific computing. Traditionally, divided differences are employed to approximate derivatives, leading often to results of dubious quality at great computational expense. Automatic differentiation (AD), by contrast, is a powerful technique for accurately evaluating derivatives of functions described in a high-level programming language. AD requires little human effort and produces derivatives without truncation error. Although there is no conceptual difference between small and large codes, applying AD to programs with hundreds of thousands of lines of code is still a challenging task and requires a robust AD tool. We report on recent accomplishments of AD applied to the general-purpose finite element package SEPRAN transforming approximately 400,000 lines of Fortran77, and its integration into a prototype problem solving environment called EFCOSS supporting interoperability of simulation codes with optimization software using AD technology.