Regularized ensemble Kalman methods for inverse problems

• Published in: Journal of computational physics Vol. 416; p. 109517
• Main Authors: Zhang, Xin-Lei, Michelén-Ströfer, Carlos, Xiao, Heng
• Format: Journal Article
• Language: English
• Published: Cambridge Elsevier Inc 01.09.2020; Elsevier Science Ltd
• Subjects: Bayesian inference; Computational fluid dynamics; Computational physics; Cost analysis; Cost function; Ensemble Kalman method; Field inversion; Ill posed problems; Inverse problems; Mathematical models; Optimization; Parameter estimation; Parameters; Regularization; Field inversion; Parameter estimation; Bayesian inference; Regularization; Ensemble Kalman method
• ISSN: 0021-9991; 1090-2716
• DOI: 10.1016/j.jcp.2020.109517

•Built general constraints into ensemble Kalman methods for inverse problems.•Derived regularization for ensemble methods equivalent to that in adjoint methods.•Bridges the gap between regularization in adjoint- and ensemble-based methods.•Requires only minor algorithmic modifications to traditional Kalman method. 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.