DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion (Communications in Computational Physics, 2021)
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 asa flexible framework for two broad classes of such inverse problems: data assimilation and field inversion. DAFI generalizes these diverse problems into a generalformulation and solves it with ensemble Kalman filters, a family of ensemble-based,derivative-free, Bayesian methods. This Bayesian approach has the added advantageof providing built-in uncertainty quantification. Moreover, the code provides tools forperforming 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 areshowcased through several test cases including state and parameter estimation for theLorenz dynamic system, field inversion for the diffusion equations, and uncertaintyquantification. The object-oriented nature of the code allows for easily interchangingdifferent solution methods and different physics problems. It provides a simple interface for the users to supply their domain-specific physics models. Finally, the codecan be used as a test-bed for new ensemble-based data assimilation and field inversionmethods.
Keywords: Data assimilation; inverse modeling; random fields; ensemble Kalman filter; Bayesian inference.