The Level Set Ensemble Kalman Filter: Sequential Data Assimilation for Flows With Shocks
Abstract
The ensemble Kalman filter is commonly used to perform sequential data assimilation in science and engineering because of its robust performance and scalability when the state space dimension is high. However, the standard EnKF generates spurious oscillations when applied to compressible flows with shocks; the cause is the uncertainty, across the ensemble, in the shock location. This paper develops the \emph{level set EnKF}, which overcomes this challenge by employing a nonlinear mapping from the original state space into a latent space in which the analysis step is undertaken. The latent representation comprises a level set function that encodes the discontinuity location and smooth state extensions, defined over the full physical domain, that represent the solution on either side of the discontinuity. We first formulate the level set EnKF as a specific instance of a more general approach of performing data assimilation in a latent space. We then demonstrate the feasibility of the level set EnKF for several one-dimensional compressible flows and a two-dimensional blast wave.
Related
- Reconstructing Rayleigh--Bénard flows from temperature-only measurements using the Ensemble Kalman Filter
- 3D kinematics using a fast extended Kalman filter relying on multiview 2D pose observations
- Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
- Online Learning of a Spectral Eddy Viscosity Closure for Large Eddy Simulation Using Ensemble Kalman Inversion
- Convolutional 2D Kalman filter & LQG control
BibTeX
@misc{sleeman2026level,
title = {{The Level Set Ensemble Kalman Filter: Sequential Data Assimilation for Flows With Shocks}},
author = {Michael K. Sleeman and Lorenzo Beronilla and Hangchuan Hu and Xu-Hui Zhou and Matthias Morzfeld and Andrew M. Stuart and Tamer A. Zaki},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.19314},
eprint = {2609.19314},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.19314},
}
RIS
TY - UNPB
TI - The Level Set Ensemble Kalman Filter: Sequential Data Assimilation for Flows With Shocks
AU - Michael K. Sleeman
AU - Lorenzo Beronilla
AU - Hangchuan Hu
AU - Xu-Hui Zhou
AU - Matthias Morzfeld
AU - Andrew M. Stuart
AU - Tamer A. Zaki
PY - 2026
DA - 2026/09/16
PB - arXiv
DO - 10.48550/arxiv.2609.19314
UR - https://arxiv.org/abs/2609.19314
AB - The ensemble Kalman filter is commonly used to perform sequential data assimilation in science and engineering because of its robust performance and scalability when the state space dimension is high. However, the standard EnKF generates spurious oscillations when applied to compressible flows with shocks; the cause is the uncertainty, across the ensemble, in the shock location. This paper develops the \emph{level set EnKF}, which overcomes this challenge by employing a nonlinear mapping from the original state space into a latent space in which the analysis step is undertaken. The latent representation comprises a level set function that encodes the discontinuity location and smooth state extensions, defined over the full physical domain, that represent the solution on either side of the discontinuity. We first formulate the level set EnKF as a specific instance of a more general approach of performing data assimilation in a latent space. We then demonstrate the feasibility of the level set EnKF for several one-dimensional compressible flows and a two-dimensional blast wave.
ER -