Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
The ensemble Kalman filter (EnKF) is widely adopted for sequential data assimilation, but fails for solutions with discontinuities, such as shocks in compressible flows. For ensembles with uncertainty in discontinuity location, the EnKF analysis can lose physical consistency, which can be viewed through three lenses: (i) bimodal pointwise statistics in the forecast ensemble, (ii) mixed-sign analysis weights in the affine superposition of forecast members, and (iii) spatially distributed Kalman-gain profiles that modify the state around a discontinuity rather than translating it coherently. In order to preserve flow features in the analysis state and, as such, maintain physical consistency...
| Authors | Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 10 June 2026 |
| Last updated at source | 14 October 2026 |
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