arrow
Volume 29, Issue 5
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion

Carlos A. Michelén Ströfer, Xin-Lei Zhang & Heng Xiao

Commun. Comput. Phys., 29 (2021), pp. 1583-1622.

Published online: 2021-03

Export citation
  • Abstract

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.

  • Keywords

Data assimilation, inverse modeling, random fields, ensemble Kalman filter, Bayesian inference.

  • AMS Subject Headings

35R30, 76M21, 60-04

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@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 = {

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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2020-0178}, url = {http://global-sci.org/intro/article_detail/cicp/18732.html} }
TY - JOUR T1 - DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion AU - A. Michelén Ströfer , Carlos AU - Zhang , Xin-Lei AU - Xiao , Heng JO - Communications in Computational Physics VL - 5 SP - 1583 EP - 1622 PY - 2021 DA - 2021/03 SN - 29 DO - http://doi.org/10.4208/cicp.OA-2020-0178 UR - https://global-sci.org/intro/article_detail/cicp/18732.html KW - Data assimilation, inverse modeling, random fields, ensemble Kalman filter, Bayesian inference. AB -

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.

Carlos A. Michelén Ströfer, Xin-Lei Zhang & Heng Xiao. (2021). DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion. Communications in Computational Physics. 29 (5). 1583-1622. doi:10.4208/cicp.OA-2020-0178
Copy to clipboard
The citation has been copied to your clipboard