A deep learning‐based ensemble filter for nonlinear data assimilation

PAMM, vol. 20

Abstract

Abstract This article presents a novel deep learning‐based ensemble conditional mean filter (DL‐EnCMF) for nonlinear data assimilation. The filter's key component is the approximation of the conditional expectation (CE) using deep neural networks (DNNs). We implement the DL‐EnCMF for tracking the states of the Lorenz‐63 system. Numerical results show that the DL‐EnCMF outperforms the ensemble Kalman filter (EnKF)—a common technique for data assimilation.

Authors 2

1. Truong‐Vinh Hoang corresponding Aachen Chair of Mathematics for Uncertainty Quantification

   RWTH Aachen University

   Affiliation as printed

   Chair of Mathematics for Uncertainty Quantification RWTH Aachen University 52056 Aachen

   Chair of Mathematics for Uncertainty Quantification, RWTH Aachen University, 52056 Aachen

   Truong-Vinh Hoang

2. Hermann Georg Matthies corresponding Aachen Chair of Mathematics for Uncertainty Quantification

   RWTH Aachen University · Technische Universität Braunschweig

   Affiliation as printed

   Institute for Scientific Computing Technische Universität Braunschweig 38106 Braunschweig

   Chair of Mathematics for Uncertainty Quantification, RWTH Aachen University, 52056 Aachen

   Truong-Vinh Hoang