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
Bringing together automatic differentiation and OpenMP
ICS '01 Proceedings of the 15th international conference on Supercomputing
Solving large-scale optimization problems with EFCOSS
Advances in Engineering Software
Using automatic differentiation to compute derivatives for a quantum-chemical computer program
Future Generation Computer Systems
Using automatic differentiation to compute derivatives for a quantum-chemical computer program
Future Generation Computer Systems
Sensitivities for a single drop simulation
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
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Derivatives are ubiquitous in various areas of computational science including sensitivity analysis and parameter optimization of computer models. Among the various methods for obtaining derivatives, automatic differentiation (AD) combines freedom from approximation errors, high performance, and the ability to handle arbitrarily complex codes arising from large-scale scientific investigations. In this note, we show how AD technology can aid in the sensitivity analysis of a computer model by considering a classic fluid flow experiment as an example. To this end, the software tool ADIFOR implementing the AD technology for functions written in Fortran 77 was applied to the large finite element package SEPRAN. Differentiated versions of SEPRAN enable sensitivity analysis for a wide range of applications, not only from computational fluid dynamics.