Concurrent MultiParameter Learning Demonstrated on the Kuramoto--Sivashinsky Equation
Benjamin Pachev, Jared P. Whitehead, Shane A. McQuarrie
Abstract
We develop an algorithm based on the nudging data assimilation scheme for the concurrent (on-the-fly) estimation of scalar parameters for a system of evolutionary dissipative partial differential equations in which the state is partially observed. The algorithm takes advantage of the error that results from nudging a system with incorrect parameters with data from the true system. The intuitive nature of the algorithm makes its extension to several different systems immediate, and it allows for recovery of multiple parameters simultaneously. We test the method on the Kuramoto--Sivashinsky equation in one dimension and demonstrate its efficacy in this context.
Topics & Concepts
MathematicsPartial differential equationEstimatorScalar (mathematics)Data assimilationDissipative systemApplied mathematicsContext (archaeology)Extension (predicate logic)Dimension (graph theory)Ordinary differential equationDiffusion equationAlgorithmMathematical optimizationDifferential equationMathematical analysisComputer scienceStatisticsEconomyQuantum mechanicsPaleontologyBiologyProgramming languagePure mathematicsPhysicsGeometryMeteorologyEconomicsService (business)Model Reduction and Neural NetworksStability and Controllability of Differential EquationsFluid Dynamics and Turbulent Flows