1. Introduction to ADAO¶
The aim of the ADAO module is to help using data assimilation or optimization methodology, in conjunction with other modules or simulation codes, in Python [Python] or SALOME context [Salome]. It provides a simple interface to many robust and powerful data assimilation or optimization algorithms, with or without reduction, as well as testing and verification aids. It allows to integrate these tools in a Python or SALOME study.
Its main objective is to provide the use of standard and robust data assimilation or optimization methods, in a usual numerical simulation study approach, in an efficient way, while remaining easy to setup, and by providing a simplified approach to help the implementation. For the end user, who has previously collected information on his physical problem, the environment allows him to have an approach centered on the simple declaration of this information to build a valid ADAO case, to then evaluate it, and to get the physical results he needs.
The module covers a wide variety of practical applications, in a robust way, allowing for real world engineering applications, and also for performing quick methodological experimentation. It is based on the use of other Python or SALOME modules, in particular YACS and EFICAS if available, and on the use of an underlying generic data assimilation library and tools. The computational or simulation user modules must provide one or more specific calling methods in order to be callable in the Python or SALOME framework. In the SALOME environment, all native modules can be used through integration in Python or YACS.