Ensemble transport smoothing. Part II: Nonlinear updates

Maximilian Ramgraber, Ricardo Baptista, Dennis McLaughlin, Youssef Marzouk

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Smoothing is a specialized form of Bayesian inference for state-space models that characterizes the posterior distribution of a collection of states given an associated sequence of observations. Ramgraber et al. [38] proposes a general framework for transport-based ensemble smoothing, which includes linear Kalman-type smoothers as special cases. Here, we build on this foundation to realize and demonstrate nonlinear backward ensemble transport smoothers. We discuss parameterization and regularization of the associated transport maps, and then examine the performance of these smoothers for nonlinear and chaotic dynamical systems that exhibit non-Gaussian behavior. In these settings, our nonlinear transport smoothers yield lower estimation error than conventional linear smoothers and state-of-the-art iterative ensemble Kalman smoothers, for comparable numbers of model evaluations.

Original languageEnglish
Article number100133
JournalJournal of Computational Physics: X
Volume17
DOIs
Publication statusPublished - Nov 2023
Externally publishedYes

Fingerprint

Dive into the research topics of 'Ensemble transport smoothing. Part II: Nonlinear updates'. Together they form a unique fingerprint.

Cite this