English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Inverse problems are more challenging when only partial data are available ingeneral. In this paper, we propose a two-step approach combining the extendedsampling method and the ensemble Kalman filter to reconstruct an elastic rigidobstacle using partial data. In the first step, the approximate location of theunknown obstacle is obtained by the extended sampling method. In the secondstep, the ensemble Kalman filter is employed to reconstruct the shape. Thelocation obtained in the first step guides the construction of the initialparticles of the ensemble Kalman filter, which is critical to the performanceof the second step. Both steps are based on the same physical model and use thesame scattering data. Numerical examples are shown to illustrate theeffectiveness of the proposed method.

An extended sampling-ensemble Kalman filter approach for partial data inverse elastic problems