scholarly journals Numerical analysis of sparse initial data identification for parabolic problems

2020 ◽  
Vol 54 (4) ◽  
pp. 1139-1180
Author(s):  
Dmitriy Leykekhman ◽  
Boris Vexler ◽  
Daniel Walter
Keyword(s):  
Numerical Analysis ◽  
Finite Elements ◽  
Initial Data ◽  
Error Estimates ◽  
Piecewise Linear ◽  
Control Variable ◽  
Parabolic Problems ◽  
Time Observation ◽  
Dirac Measures ◽  
Objective Functional

In this paper we consider a problem of initial data identification from the final time observation for homogeneous parabolic problems. It is well-known that such problems are exponentially ill-posed due to the strong smoothing property of parabolic equations. We are interested in a situation when the initial data we intend to recover is known to be sparse, i.e. its support has Lebesgue measure zero. We formulate the problem as an optimal control problem and incorporate the information on the sparsity of the unknown initial data into the structure of the objective functional. In particular, we are looking for the control variable in the space of regular Borel measures and use the corresponding norm as a regularization term in the objective functional. This leads to a convex but non-smooth optimization problem. For the discretization we use continuous piecewise linear finite elements in space and discontinuous Galerkin finite elements of arbitrary degree in time. For the general case we establish error estimates for the state variable. Under a certain structural assumption, we show that the control variable consists of a finite linear combination of Dirac measures. For this case we obtain error estimates for the locations of Dirac measures as well as for the corresponding coefficients. The key to the numerical analysis are the sharp smoothing type pointwise finite element error estimates for homogeneous parabolic problems, which are of independent interest. Moreover, we discuss an efficient algorithmic approach to the problem and show several numerical experiments illustrating our theoretical results.

Composite Finite Element Approximation for Parabolic Problems in Nonconvex Polygonal Domains

2020 ◽  
Vol 20 (2) ◽  
pp. 361-378
Author(s):  
Tamal Pramanick ◽  
Rajen Kumar Sinha
Keyword(s):  
Finite Element ◽  
Finite Elements ◽  
Error Estimates ◽  
Finite Element Approximation ◽  
Parabolic Problems ◽  
Element Approximation ◽  
Dirichlet Problems ◽  
Element Mesh ◽  
Polygonal Domains ◽  
Numer Math

AbstractThe purpose of this paper is to generalize known a priori error estimates of the composite finite element (CFE) approximations of elliptic problems in nonconvex polygonal domains to the time dependent parabolic problems. This is a new class of finite elements which was introduced by [W. Hackbusch and S. A. Sauter, Composite finite elements for the approximation of PDEs on domains with complicated micro-structures, Numer. Math. 75 1997, 4, 447–472] and subsequently modified by [M. Rech, S. A. Sauter and A. Smolianski, Two-scale composite finite element method for Dirichlet problems on complicated domains, Numer. Math. 102 2006, 4, 681–708] for the approximations of stationery problems on complicated domains. The basic idea of the CFE procedure is to work with fewer degrees of freedom by allowing finite element mesh to resolve the domain boundaries and to preserve the asymptotic order convergence on coarse-scale mesh. We analyze both semidiscrete and fully discrete CFE methods for parabolic problems in two-dimensional nonconvex polygonal domains and derive error estimates of order {\mathcal{O}(H^{s}\widehat{\mathrm{Log}}{}^{\frac{s}{2}}(\frac{H}{h}))} and {\mathcal{O}(H^{2s}\widehat{\mathrm{Log}}{}^{s}(\frac{H}{h}))} in the {L^{\infty}(H^{1})}-norm and {L^{\infty}(L^{2})}-norm, respectively. Moreover, for homogeneous equations, error estimates are derived for nonsmooth initial data. Numerical results are presented to support the theoretical rates of convergence.

scholarly journals The analysis of gas filtration model with use of fractional derivatives in time

2017 ◽  
pp. 82-89
Author(s):  
Nazariy Lopuh
Keyword(s):  
Numerical Analysis ◽  
Numerical Model ◽  
Finite Elements ◽  
Initial Data ◽  
Pressure Distribution ◽  
Fractional Derivatives ◽  
Finite Elements Method ◽  
Gas Filtration ◽  
Definition Of ◽  
Filtration Model

In work on the basis of a finite elements method it is offered numerical model of gas filtration in porous non-uniform environments with use of fractional derivatives in time. Kaputto and Rimman- Liouville's fractional derivatives are considered. The numerical analysis with use of experimental initial data is made. The received results can be used for a research of filtrational properties of the vicinity of the well, definition of its output depending on pressure distribution.

Error estimates for Neumann boundary control problems with energy regularization

2016 ◽  
Vol 24 (4) ◽  
Author(s):  
Thomas Apel ◽  
Olaf Steinbach ◽  
Max Winkler
Keyword(s):  
Error Estimates ◽  
Boundary Control ◽  
Convergence Rates ◽  
Piecewise Linear ◽  
Control Variable ◽  
Neumann Boundary ◽  
State Equation ◽  
Piecewise Constant ◽  
Boundary Control Problems ◽  
Unconstrained Problems

AbstractA Neumann boundary control problem for a second order elliptic state equation is considered. The problem is regularized by an energy term which is equivalent to theThe state and co-state are approximated by piecewise linear finite elements. For the approximation of the control variable we use carefully designed spaces of piecewise linear or piecewise constant functions, such that an inf-sup condition is satisfied. Error estimates for the approximate solution are proved for all three variables and we show a relation between convergence rate and the opening angles at corners of the domain. As the control grows in general unboundedly near the concave corners for unconstrained problems, it becomes active and hence regular when control constraints are present. We show that in this case the convergence rates are higher than in the unbounded case. Numerical tests suggest that the estimates derived are optimal in the unconstrained case but too pessimistic in the control constrained case.

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