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DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion

DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion

Year:    2021

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

Communications in Computational Physics, Vol. 29 (2021), Iss. 5 : pp. 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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.OA-2020-0178

Communications in Computational Physics, Vol. 29 (2021), Iss. 5 : pp. 1583–1622

Published online:    2021-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    40

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

Author Details

Carlos A. Michelén Ströfer

Xin-Lei Zhang

Heng Xiao

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