Ensemble variational Fokker-Planck methods for data assimilation
Authors:
- Amit N. SubrahmanyaASAmit N. Subrahmanya
- Andrey A. PopovAPAndrey A. Popov
- Adrian SanduASAdrian Sandu
- 19 December 2024
- journal article
- research article
- Published by Elsevier in Journal of Computational Physics
- Vol. 523
Abstract
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Funding Information
- Advanced Scientific Computing Research (ASCR DE-SC0021313)
- U.S. Department of Energy
- National Science Foundation (CDS&E\u2013MSS 1953113)
This publication has 50 references indexed in Scilit:
- A particle flow filter for high‐dimensional system applicationsQuarterly Journal of the Royal Meteorological Society, 2021
- Particle filters for high‐dimensional geoscience applications: A reviewQuarterly Journal of the Royal Meteorological Society, 2019
- Ensemble Kalman filter implementations based on shrinkage covariance matrix estimationOcean Dynamics, 2015
- A Gaussian‐mixture ensemble transform filterQuarterly Journal of the Royal Meteorological Society, 2011
- A localization technique for ensemble Kalman filtersQuarterly Journal of the Royal Meteorological Society, 2010
- The Ensemble Kalman Filter: theoretical formulation and practical implementationOcean Dynamics, 2003
- Adjoint Implementation of Rosenbrock Methods Applied to Variational Data Assimilation ProblemsJournal of Computational Physics, 2000
- A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problemNumerische Mathematik, 2000
- Existence and Uniqueness of a Global Smooth Solution for the Vlasov-Poisson-Fokker-Planck System in Three DimensionsJournal of Functional Analysis, 1993
- ber die analytischen Methoden in der WahrscheinlichkeitsrechnungMathematische Annalen, 1931