Add initial outline of documentation for the AD/SD module.

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+// ---------------------------------------------------------------------
+//
+// Copyright (C) 2017 by the deal.II authors
+//
+// This file is part of the deal.II library.
+//
+// The deal.II library is free software; you can use it, redistribute
+// it, and/or modify it under the terms of the GNU Lesser General
+// Public License as published by the Free Software Foundation; either
+// version 2.1 of the License, or (at your option) any later version.
+// The full text of the license can be found in the file LICENSE at
+// the top level of the deal.II distribution.
+//
+// ---------------------------------------------------------------------
+
+/**
+ * @defgroup auto_symb_diff Automatic and symbolic differentiation
+ *
+ * @brief A module dedicated to the implementation of functions and classes that relate
+ * to automatic and symbolic differentiation.
+ *
+ * @todo Hyper-summarize the following list of topics:
+ * - Automatic differentiation
+ * - Symbolic differentiation
+ *
+ * @section auto_diff_1 Automatic differentiation
+ *
+ * @todo Write a short introduction into AD. As a temporary entry, the following links
+ * may be enlightening:
+ * - <a href="https://en.wikipedia.org/wiki/Automatic_differentiation">Wikipedia article</a>
+ * - <a href="https://projects.coin-or.org/ADOL-C/browser/stable/2.6/ADOL-C/doc/adolc-manual.pdf?format=raw#page=1>Adol-C manual</a>
+ *
+ * @todo Hyper-summarize the following list of topics:
+ * - Forward and reverse mode AD
+ * - Taped and tapeless AD; expression templates
+ *
+ * @subsection auto_diff_1_1 Supported automatic differentiation libraries
+ *
+ * We currently have validated implementations for the following number types
+ * and combinations:
+ *
+ *  - Taped Adol-C (n-differentiable, in theory, but internal drivers for up to second-order
+ *    derivatives have been implemented)
+ *  - Tapeless Adol-C (once differentiable)
+ *  - Tapeless forward-mode Sacado with dynamic memory allocation (once differentiable)
+ *  - Tapeless nested forward-mode Sacado (twice differentiable)
+ *  - Tapeless reverse-mode Sacado (once differentiable)
+ *  - Tapeless nested reverse and forward-mode Sacado (twice differentiable)
+ *
+ * @subsection auto_diff_1_2 How automatic differentiation is integrated into deal.II
+ *
+ * Since the interface to each automatic differentiation library is so vastly different,
+ * a uniform internal interface to each number has been established. This allows the
+ * driver classes (that provide the core functionality, and are introduced in the next
+ * section) a consistent mechanism to interact with different auto-differentiation
+ * libraries. Specifically, they need to be able to correctly initialize and finalize data
+ * that is to be interpreted as the dependent and independent variables of a formula.
+ *
+ * A summary of the files that implement the interface to the supported auto-differentiable
+ * numbers is as follows:
+ *
+ * - ad_number_types.h: Introduces an enumeration (called a type code) for the
+ *   auto-differentiable number combinations that will be supported by the driver classes.
+ *   The rationale behind the use of this somewhat restrictive mechanism is discussed below.
+ * - ad_number_traits.h: Declare some internal classes that are to be specialized for
+ *   each auto-differentiation library and/or number type. These are subsequently used to
+ *   provide a uniform interface to the classes through the NumberTraits and ADNumberTraits
+ *   classes which are extensively used throughout of drivers. We also provide some mechanisms
+ *   to easily query select properties of these numbers, i.e. some type traits.
+ * - adolc_number_types.h: Implementation of the internal classes that define how we
+ *   use Adol-C numbers.
+ * - adolc_product_types.h: Defines some product and scalar types that allow the use of
+ *   Adol-C numbers in conjunction with the Tensor and SymmetricTensor classes.
+ * - sacado_number_types.h: Implementation of the internal classes that define how we
+ *   use the supported Sacado numbers.
+ * - sacado_product_types.h: Defines some product and scalar types that allow the use of
+ *   the supported Sacado numbers in conjunction with the Tensor and SymmetricTensor
+ *   classes.
+ *
+ * By using type codes for each supported number type, we artificially limit the type
+ * of auto-differentiable numbers that can be used within the library. This design choice
+ * is due to the fact that its not trivial to ensure that each number type is correctly
+ * initialized and that all combinations of nested (templated) types remain valid for all
+ * operations performed by the library.
+ * Furthermore, there are some lengthy functions within the library that are instantiated
+ * for the supported number types and have internal checks that are only satisfied when a
+ * auto-differentiable number, of which the library has knowledge, is used. This again
+ * ensures that the integrity of all computations is maintained.
+ * Finally, using a simple enumeration as a class template parameter ultimately makes it
+ * really easy to switch between the type used in production code with little to no further
+ * amendments required to user code.
+ *
+ * @subsubsection auto_diff_1_3 User interface to the automatic differentiation libraries
+ *
+ * @todo Summarize driver classes 
+ *
+ */