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Iterative ensemble Kalman methods: A unified perspective with some new variants

  • * Corresponding author: Daniel Sanz-Alonso

    * Corresponding author: Daniel Sanz-Alonso
NKC is supported by KAUST baseline funding. DSA is thankful for the support of NSF and NGA through the grant DMS-2027056. The work of DSA was also partially supported by the NSF Grant DMS-1912818/1912802
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  • This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We identify, compare and develop three subfamilies of ensemble methods that differ in the objective they seek to minimize and the derivative-based optimization scheme they approximate through the ensemble. Our work emphasizes two principles for the derivation and analysis of iterative ensemble Kalman methods: statistical linearization and continuum limits. Following these guiding principles, we introduce new iterative ensemble Kalman methods that show promising numerical performance in Bayesian inverse problems, data assimilation and machine learning tasks.

    Mathematics Subject Classification: Primary: 37C10, 60G35; Secondary: 65N20.


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  • Figure 1.  Ensemble members (green) after $ 100 $ iterations, with truth $ u^\dagger $ (red star) and contour plot of (unnormalized) posterior density

    Figure 2.  Evolution of the Frobenius norm of the ensemble covariance $ P^{uu}(t) $. For reference, we also plot the Frobenius norm of the true posterior covariance (red dashed line). The norm of IEKF-RZL blows up after a few iterations. The norms of the EKI and TEKI are almost identical and monotonically decreasing. The norms of the new variants EKI-SL and IEKF-SL are similar and stabilize after around 40 iterations. The norm of IEKF lies between those of the old and new variants

    Figure 3.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $

    Figure 4.  TEKI, IEKF & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $

    Figure 5.  Ensemble mean (red) at the final iteration, with 10, 90-quantiles (blue)

    Figure 6.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $

    Figure 7.  IEKF, TEKI & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $

    Figure 8.  Ensemble mean (red) at the final iteration, with 10, 90-quantiles (blue)

    Figure 9.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $

    Figure 10.  IEKF, TEKI & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $

    Figure 11.  Tikhonov-Phillips objective function with respect to two randomly chosen coordinates

    Figure 12.  Ensemble mean (red) at the final iteration, with 10, 90-quantiles (blue)

    Figure 13.  EKI & EKI-SL: Relative errors and data misfit w.r.t time $ t $

    Figure 14.  IEKF, TEKI & IEKF-SL: Relative errors and Tikhonov-Phillips objective w.r.t time $ t $

    Table 1.  Roadmap to the algorithms considered in this paper. We use the abbreviations GN and LM for Gauss-Newton and Levenberg-Marquardt. The numbers in parenthesis represent the subsection in which each algorithm is introduced

    Objective Optimization Derivative Method Ensemble Method New Variant
    $ {\mathtt{J}}_{\text{TP}} $ GN IExKF (2.1) IEKF (3.1) IEKF-SL (4.1)
    $ {\mathtt{J}}_{\text{DM}} $ LM LM-DM (2.2) EKI (3.2) EKI-SL (4.2)
    $ {\mathtt{J}}_{\text{TP}} $ LM LM-TP (2.3) TEKI (3.3) TEKI-SL (4.3)
     | Show Table
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    Table 2.  Summary of the main algorithms in Sections 2 and 3

    Objective Optimization Derivative Method Ensemble Method
    $ {\mathtt{J}}_{\text{TP}} $ GN IExKF IEKF
    $ {\mathtt{J}}_{\text{DM}} $ LM ILM-DM EKI
    $ {\mathtt{J}}_{\text{TP}} $ LM ILM-TP TEKI
     | Show Table
    DownLoad: CSV
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