Gradient free descent: shadowing, and state estimation using limited derivative information

Kevin Judd, L. Smith, A. Weisheimer

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Shadowing trajectories can play an important role in assessing the reliability of forecasting models, they can also play an important role in providing state estimates for ensemble forecasts. Gradient descent methods provide one approach for obtaining shadowing trajectories, which have been shown to have many useful properties. There remains the important question whether shadowing trajectories can be found in very high-dimensional systems, like weather and climate models. The principle impediment is the need to compute the derivative (or adjoint) of the system dynamics. In this paper we investigate gradient descent methods that use limited derivative information. We demonstrate the methods with an application to a moderately high-dimensional system using no derivative information at all. (C) 2003 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)153-166
JournalPhysica D-Nonlinear Phenomena
Volume190
Issue number3-4
DOIs
Publication statusPublished - 2004

Fingerprint Dive into the research topics of 'Gradient free descent: shadowing, and state estimation using limited derivative information'. Together they form a unique fingerprint.

Cite this