ADAO documentation

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The ADAO module provides data assimilation and optimization features in Python [Python] or SALOME context [Salome].

Briefly stated, Data Assimilation is a methodological framework to compute the optimal estimate of the inaccessible true value of a system state, eventually 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 under the names of calibration, adjustment, state estimation, parameter estimation, parameter adjustment, inverse problems, Bayesian estimation, optimal interpolation, mathematical regularization, meta-heuristics for optimization, model reduction, data smoothing, etc. More details can be found in the section [DocT] A brief introduction to Data Assimilation and Optimization. The ADAO module currently offers more than one hundred different algorithmic methods and allows the study of about 350 distinct applied problems.

The documentation for this module is divided into several major categories, related to the theoretical documentation (indicated in the section title by [DocT]), to the user documentation (indicated in the section title by [DocU]), and to the reference documentation (indicated in the section title by [DocR]).

The first part is the Introduction to ADAO. The second part introduces [DocT] A brief introduction to Data Assimilation and Optimization, and their concepts, and the next part describes the [DocT] Methodology to elaborate a Data Assimilation or Optimization study. For a standard user, the next parts describe examples on ADAO usage as [DocU] Tutorials on using the ADAO module in SALOME or [DocU] Tutorials on using the ADAO module in Python, then indicates the [DocU] Advanced usage of the ADAO module, with how to obtain additional information or how to use non-GUI command execution scripting. Users interested in quick use of the module can stop before reading the rest, but a valuable use of the module requires to read and come back regularly to these parts. The following parts describe [DocR] Graphical User Interface for ADAO (GUI/EFICAS) and [DocR] Textual User Interface for ADAO (TUI/API). The last main part gives a detailed [DocR] Reference description of the ADAO commands and keywords, with three essential main sub-parts describing the details of commands and options of the algorithms. A Glossary, some Notations and common conventions, a Bibliography and an extensive index are included in the document. And, to comply with the module requirements, be sure to read the part License and requirements.

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