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Quasi-Convergence of an Implementation of Optimal Balance by Backward-Forward Nudging


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Masur, Gökce Tuba ; Mohamad, Haidar ; Oliver, Marcel:
Quasi-Convergence of an Implementation of Optimal Balance by Backward-Forward Nudging.
In: Multiscale modeling & simulation. 21 (2023) 2. - S. 624-640.
ISSN 1540-3467 ; 1540-3459


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Optimal balance is a nonasymptotic numerical method for computing a point on an elliptic slow manifold for two-scale dynamical systems with strong gyroscopic forces. It works by solving a modified differential equation as a boundary value problem in time, where the nonlinear terms are adiabatically ramped up from zero to the fully nonlinear dynamics. A dedicated boundary value solver, however, is often not directly available. The most natural alternative is a nudging solver, where the problem is repeatedly solved forward and backward in time and the respective boundary conditions are restored whenever one of the temporal end points is visited. In this paper, we show quasi-convergence of this scheme in the sense that the termination residual of the nudging iteration is as small as the asymptotic error of the method itself, i.e., under appropriate assumptions exponentially small. This confirms that optimal balance in its nudging formulation is an effective algorithm. Further, it shows that the boundary value problem formulation of optimal balance is well posed up to at most a residual error as small as the asymptotic error of the method itself. The key step in our proof is a careful two-component Gronwall inequality.

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Sprache des Eintrags:Englisch
Institutionen der Universität:Mathematisch-Geographische Fakultät > Mathematik > Lehrstuhl für Mathematik - Angewandte Mathematik
Mathematisch-Geographische Fakultät > Mathematik > Mathematisches Institut für Maschinelles Lernen und Data Science (MIDS)
DOI / URN / ID:10.1137/22M1506018
Verlag:Society for Industrial and Applied Mathematics
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Titel an der KU entstanden:Ja
Eingestellt am: 30. Apr 2024 15:07
Letzte Änderung: 06. Mai 2024 12:17
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