:align: center
:scale: 10%
-The ADAO module provides **data assimilation** features in SALOME context. It is
-based on usage of other SALOME modules, namely YACS and EFICAS, and on usage of
-a generic underlying data assimilation library.
+The ADAO module provides **data assimilation and optimization** features in
+SALOME context. It is based on usage of other SALOME modules, namely YACS and
+EFICAS, and on usage of a generic underlying data assimilation library.
Briefly stated, Data Assimilation is a methodological framework to compute the
optimal estimate of the inaccessible true value of a system state over time. It
uses information coming from experimental measurements or observations, and from
numerical *a priori* models, including information about their errors. Parts of
-the framework are also known as *parameter estimation*, *inverse problems*,
-*bayesian estimation*, *optimal interpolation*, etc. More details can be found
-in the section :ref:`section_theory`.
-
-The documentation of this module is divided in 5 parts, the first one being an
-introduction. The second part briefly introduces data assimilation and concepts.
-The third part describes how to use the module ADAO. The fourth part gives
-examples on ADAO usage. Users interested in quick use of the module can jump to
-this section :ref:`section_examples`, but a valuable use of the module requires
-to read and come back regularly to the section :ref:`section_using`. The last
-part focuses on advanced usages of the module, how to get more information, or
-how to use it without the graphical user interface (GUI).
-
-In all this documentation, we use standard notations of data assimilation, as
-described in [Ide97]. Moreover, vectors are written horizontally or vertically
-without making difference. Matrices are written either normally, or with a
-condensed notation, consisting in the use of a space to separate values and a
-"``;``" to separate the rows, in a continuous line.
+the framework are also known under the names of *parameter estimation*, *inverse
+problems*, *Bayesian estimation*, *optimal interpolation*, etc. More details can
+be found in the section :ref:`section_theory`.
+
+The documentation of this module is divided in parts. The first one
+:ref:`section_intro` is an introduction. The second part :ref:`section_theory`
+briefly introduces data assimilation, optimization and concepts. The third part
+:ref:`section_using` describes how to use the module ADAO. The fourth part
+:ref:`section_reference` gives a detailed description of all the ADAO commands
+and keywords. The fifth part :ref:`section_examples` gives examples on ADAO
+usage. Users interested in quick use of the module can jump to this section, but
+a valuable use of the module requires to read and come back regularly to the
+third and fourth ones. The last part :ref:`section_advanced` focuses on advanced
+usages of the module, how to get more information, or how to use it by
+scripting, without the graphical user interface (GUI).
+
+In all this documentation, we use standard notations of linear algebra, data
+assimilation (as described in [Ide97]_) and optimization. In particular, vectors
+are written horizontally or vertically without making difference. Matrices are
+written either normally, or with a condensed notation, consisting in the use of
+a space to separate values and a "``;``" to separate the rows, in a continuous
+line.
Table of contents
-----------------
intro
theory
using
+ reference
examples
advanced
bibliography
