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

TL;DR

DAFI is an open-source, flexible framework that uses ensemble Kalman filters for inverse problems like data assimilation and field inversion, providing uncertainty quantification and easy integration with other tools.

Contribution

It introduces a general, object-oriented framework for ensemble-based inverse problems, supporting multiple physics models and serving as a test-bed for new methods.

Findings

Successfully applied to Lorenz system state estimation

Demonstrated field inversion for diffusion equations

Showcased uncertainty quantification capabilities

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.