@Misc{symengine-web-page,
key = {symengine},
title = {{SymEngine: fast symbolic manipulation library, written in C++}},
- note = {\url{https://github.com/symengine/symengine}, \url{http://sympy.org/}}}
+ note = {\url{https://symengine.org/}}}
@Misc{hdf5,
author = {{The HDF Group}},
\item \texttt{step-68} TODO
\item \texttt{step-71} focuses on automatic and symbolic
- differentiation (AD and SD, in short) as a tool to make solvers for complex, nonlinear
- problems possible. To this end, \dealii{} can interface to a number
- of AD and SD libraries, including Trilinos' Sacado package
- \cite{...}, ...\todo{which others??}, and SymEngine \cite{symengine-web-page}. The tutorial
+ differentiation (AD and SD, in short) as a tool to make solvers for complex,
+ nonlinear problems possible. To this end, \dealii{} can interface to a number
+ of AD and SD libraries, specifically Trilinos' Sacado package
+ \cite{Bartlett2006a}, ADOL-C \cite{Griewank1996a}, and
+ SymEngine \cite{symengine-web-page}. The tutorial
illustrates how these techniques can be used to compute derivatives
of first a rather simple function, and then of the much more complex
- energy functions of two magneto-elastic and magneto-visco-elastic
+ energy functions of two magnetoelastic and magneto-viscoelastic
material formulations in which just the scalar energy functional
takes up the better part of a page, and even first derivatives can
only be computed with heroic effort.