Ensemble Kalman methods with constraints. (21st August 2019)

• Record Type: Journal Article
• Authors: Albers, David J; Blancquart, Paul-Adrien; Levine, Matthew E; Esmaeilzadeh Seylabi, Elnaz; Stuart, Andrew
• Is Part Of: Inverse problems. Volume 35:Number 9(2019)
• Publication Date: 2019-08-21
• Subjects: ensemble Kalman methods -- equality and inequality constraints -- derivative-free optimization -- convex optimization; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/ab1c09
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it.