Regularized Ensemble Kalman Methods for Inverse Problems

TL;DR

This paper introduces a regularized ensemble Kalman method that enforces constraints during inverse problem solving, improving parameter inference especially in complex fluid dynamics applications.

Contribution

It develops a novel regularized analysis scheme for ensemble Kalman methods that implicitly minimizes a constrained cost function, enhancing inverse problem regularization.

Findings

Successfully regularized inverse problems with increasing complexity

Improved inference of scalar model parameters

Effective inference of closure fields in fluid dynamics

Abstract

Inverse problems are common and important in many applications in computational physics but are inherently ill-posed with many possible model parameters resulting in satisfactory results in the observation space. When solving the inverse problem with adjoint-based optimization, the problem can be regularized by adding additional constraints in the cost function. However, similar regularizations have not been used in ensemble-based methods, where the same optimization is done implicitly through the analysis step rather than through explicit minimization of the cost function. Ensemble-based methods, and in particular ensemble Kalman methods, have gained popularity in practice where physics models typically do not have readily available adjoint capabilities. While the model outputs can be improved by incorporating observations using these methods, the lack of regularization means the inference of the model parameters remains ill-posed. Here we propose a regularized ensemble Kalman method capable of enforcing regularization constraints. Specifically, we derive a modified analysis scheme that implicitly minimizes a cost function with generalized constraints. We demonstrate the method's ability to regularize the inverse problem with three cases of increasing complexity, starting with inferring scalar model parameters. As a final case, we utilize the proposed method to infer the closure field in the Reynolds-averaged Navier--Stokes equations; a problem of significant importance in fluid dynamics and many engineering applications.