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Verdandi User's Guide

Verdandi is a generic C++ library for data assimilation.

Verdandi is currently developed at INRIA. It aims at providing methods and tools for data assimilation. It is designed to be relevant to a large class of problems involving high-dimensional numerical models.

To guarantee the highest performance, the library is implemented in C++. In addition, Verdandi provides a Python interface generated by Swig.

Models implemented in Fortran, C, C++, Python, ... can be plugged to Verdandi using either a C++ or Python interface.

Verdandi is provided under the GNU Lesser General Public License (LGPL).

Scientific context

Data assimilation is the process of combining different sources of information in order to better estimate the state of a system. By extension, some parameters can also be estimated. These methods were originally introduced to deal with uncertainties present in models pertaining mostly to geophysics, but it is now widely recognized that they have a tremendous potential in many other applications (see euHeart example below).

Whether the system be biological, environmental, mechanical, etc., the main sources of information are always a numerical model, observations and error statistics. Data assimilation methods can be written independently of the system to which they are applied, and each method can be applied to a wide class of systems. Therefore methods are generic and can be put together in a library.

What is Verdandi for?

What is Verdandi designed for?

  • to provide data assimilation methods to non-specialists;
  • to facilitate the application of methods to a great number of problems;
  • to provide a framework for perennial development;
  • to improve the diffusion and impact of data assimilation algorithms.

Who can be a Verdandi user?

  • non-specialists, engineers or researchers, who could directly use the available data assimilation methods;
  • a specialist taking advantage of a modular framework, which should ease development, transfers and interactions.

The users provide the numerical model and the observations with the appropriate interface (see the pages models and Observations).

Acknowledgment

The development of Verdandi is financially supported by the European research initiative euHeart which aims at developing, sharing and integrating patient-specific multi-physics and multi-level models of the heart and great vessels in normal and pathological conditions to address clinical challenges. In this project data assimilation is thus intended to allow the personalization of the biophysical models considered in order to perform genuinely patient-specific simulations.

Since 2011, Verdandi is also partly supported by the European research initiative VPH-Share, specifically as regards high-performance computing features. VPH-Share aims at developing the infostructure and integrating the optimized services to expose and share data and knowledge, jointly developing multiscale models for the composition of new VPH workflows, and facilitating collaborations within the VPH community.

Verdandi homepage: http://verdandi.gforge.inria.fr/.