13,112 research outputs found
Optimized Schwarz waveform relaxation for Primitive Equations of the ocean
In this article we are interested in the derivation of efficient domain
decomposition methods for the viscous primitive equations of the ocean. We
consider the rotating 3d incompressible hydrostatic Navier-Stokes equations
with free surface. Performing an asymptotic analysis of the system with respect
to the Rossby number, we compute an approximated Dirichlet to Neumann operator
and build an optimized Schwarz waveform relaxation algorithm. We establish the
well-posedness of this algorithm and present some numerical results to
illustrate the method
A residual based snapshot location strategy for POD in distributed optimal control of linear parabolic equations
In this paper we study the approximation of a distributed optimal control
problem for linear para\-bolic PDEs with model order reduction based on Proper
Orthogonal Decomposition (POD-MOR). POD-MOR is a Galerkin approach where the
basis functions are obtained upon information contained in time snapshots of
the parabolic PDE related to given input data. In the present work we show that
for POD-MOR in optimal control of parabolic equations it is important to have
knowledge about the controlled system at the right time instances. For the
determination of the time instances (snapshot locations) we propose an
a-posteriori error control concept which is based on a reformulation of the
optimality system of the underlying optimal control problem as a second order
in time and fourth order in space elliptic system which is approximated by a
space-time finite element method. Finally, we present numerical tests to
illustrate our approach and to show the effectiveness of the method in
comparison to existing approaches
Backstepping PDE Design: A Convex Optimization Approach
Abstract\u2014Backstepping design for boundary linear PDE is
formulated as a convex optimization problem. Some classes of
parabolic PDEs and a first-order hyperbolic PDE are studied,
with particular attention to non-strict feedback structures. Based
on the compactness of the Volterra and Fredholm-type operators
involved, their Kernels are approximated via polynomial
functions. The resulting Kernel-PDEs are optimized using Sumof-
Squares (SOS) decomposition and solved via semidefinite
programming, with sufficient precision to guarantee the stability
of the system in the L2-norm. This formulation allows optimizing
extra degrees of freedom where the Kernel-PDEs are included
as constraints. Uniqueness and invertibility of the Fredholm-type
transformation are proved for polynomial Kernels in the space
of continuous functions. The effectiveness and limitations of the
approach proposed are illustrated by numerical solutions of some
Kernel-PDEs
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