A deep learning‐based ensemble filter for nonlinear data assimilation. Issue 1 (25th January 2021)

• Record Type: Journal Article
• Authors: Hoang, Truong-Vinh; Matthies, Hermann G.
• Other Names: Kuhl D. guestEditor.; Meister A. guestEditor.; Ricoeur A. guestEditor.; Wünsch O. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 20:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-25
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.202000320
• Languages: English
• ISSNs: 1617-7061

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.