@Article{CiCP-29-1583,
author = {A. Michelén Ströfer , CarlosZhang , Xin-Lei and Xiao , Heng},
title = {DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion},
journal = {Communications in Computational Physics},
year = {2021},
volume = {29},
number = {5},
pages = {1583--1622},
abstract = {<p style="text-align: justify;">In many areas of science and engineering, it is a common task to infer physical fields from sparse observations. This paper presents the DAFI code intended as
a flexible framework for two broad classes of such inverse problems: data assimilation and field inversion. DAFI generalizes these diverse problems into a general
formulation and solves it with ensemble Kalman filters, a family of ensemble-based,
derivative-free, Bayesian methods. This Bayesian approach has the added advantage
of providing built-in uncertainty quantification. Moreover, the code provides tools for
performing common tasks related to random fields, as well as I/O utilities for integration with the open-source finite volume tool OpenFOAM. The code capabilities are
showcased through several test cases including state and parameter estimation for the
Lorenz dynamic system, field inversion for the diffusion equations, and uncertainty
quantification. The object-oriented nature of the code allows for easily interchanging
different solution methods and different physics problems. It provides a simple interface for the users to supply their domain-specific physics models. Finally, the code
can be used as a test-bed for new ensemble-based data assimilation and field inversion
methods.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2020-0178},
url = {http://global-sci.org/intro/article_detail/cicp/18732.html}
}