1,424 research outputs found
Numerical controllability of the wave equation through primal methods and Carleman estimates
This paper deals with the numerical computation of boundary null controls for
the 1D wave equation with a potential. The goal is to compute an approximation
of controls that drive the solution from a prescribed initial state to zero at
a large enough controllability time. We do not use in this work duality
arguments but explore instead a direct approach in the framework of global
Carleman estimates. More precisely, we consider the control that minimizes over
the class of admissible null controls a functional involving weighted integrals
of the state and of the control. The optimality conditions show that both the
optimal control and the associated state are expressed in terms of a new
variable, the solution of a fourth-order elliptic problem defined in the
space-time domain. We first prove that, for some specific weights determined by
the global Carleman inequalities for the wave equation, this problem is
well-posed. Then, in the framework of the finite element method, we introduce a
family of finite-dimensional approximate control problems and we prove a strong
convergence result. Numerical experiments confirm the analysis. We complete our
study with several comments
Optimal shape and location of sensors for parabolic equations with random initial data
In this article, we consider parabolic equations on a bounded open connected
subset of . We model and investigate the problem of optimal
shape and location of the observation domain having a prescribed measure. This
problem is motivated by the question of knowing how to shape and place sensors
in some domain in order to maximize the quality of the observation: for
instance, what is the optimal location and shape of a thermometer? We show that
it is relevant to consider a spectral optimal design problem corresponding to
an average of the classical observability inequality over random initial data,
where the unknown ranges over the set of all possible measurable subsets of
of fixed measure. We prove that, under appropriate sufficient spectral
assumptions, this optimal design problem has a unique solution, depending only
on a finite number of modes, and that the optimal domain is semi-analytic and
thus has a finite number of connected components. This result is in strong
contrast with hyperbolic conservative equations (wave and Schr\"odinger)
studied in [56] for which relaxation does occur. We also provide examples of
applications to anomalous diffusion or to the Stokes equations. In the case
where the underlying operator is any positive (possible fractional) power of
the negative of the Dirichlet-Laplacian, we show that, surprisingly enough, the
complexity of the optimal domain may strongly depend on both the geometry of
the domain and on the positive power. The results are illustrated with several
numerical simulations
Solving forward and inverse Helmholtz equations via controllability methods
Waves are useful for probing an unknown medium by illuminating it with a source.
To infer the characteristics of the medium from (boundary) measurements,
for instance, one typically formulates inverse scattering problems
in frequency domain as a PDE-constrained optimization problem.
Finding the medium, where the simulated wave field
matches the measured (real) wave field, the inverse problem
requires the repeated solutions of forward (Helmholtz) problems.
Typically, standard numerical methods, e.g. direct solvers or iterative methods,
are used to solve the forward problem.
However, large-scaled (or high-frequent) scattering problems
are known being competitive in computation and storage for standard methods.
Moreover, since the optimization problem is severely ill-posed
and has a large number of
local minima, the inverse problem requires additional regularization
akin to minimizing the total variation.
Finding a suitable regularization for the inverse problem is critical
to tackle the ill-posedness and to reduce the computational cost and storage requirement.
In my thesis, we first apply standard methods to forward problems.
Then, we consider the controllability method (CM)
for solving the forward problem: it
instead reformulates the problem in the time domain
and seeks the time-harmonic solution of the corresponding wave equation.
By iteratively reducing the mismatch between the solution at
initial time and after one period with the conjugate gradient (CG) method,
the CMCG method greatly speeds up the convergence to the time-harmonic
asymptotic limit. Moreover, each conjugate gradient iteration
solely relies on standard numerical algorithms,
which are inherently parallel and robust against higher frequencies.
Based on the original CM, introduced in 1994 by Bristeau et al.,
for sound-soft scattering problems, we extend the CMCG method to
general boundary-value problems governed by the Helmholtz equation.
Numerical results not only show the usefulness, robustness, and efficiency
of the CMCG method for solving the forward problem,
but also demonstrate remarkably accurate solutions.
Second, we formulate the PDE-constrained optimization
problem governed by the inverse scattering problem
to reconstruct the unknown medium.
Instead of a grid-based discrete representation combined with
standard Tikhonov-type regularization, the unknown medium is
projected to a small finite-dimensional subspace,
which is iteratively adapted using dynamic thresholding.
The adaptive (spectral) space is governed by solving
several Poisson-type eigenvalue problems.
To tackle the ill-posedness that the Newton-type optimization
method converges to a false local minimum,
we combine the adaptive spectral inversion (ASI) method with the frequency stepping strategy.
Numerical examples illustrate the usefulness of the ASI approach,
which not only efficiently and remarkably reduces the dimension of the
solution space, but also yields an accurate and robust method
Experimental Study of the HUM Control Operator for Linear Waves
We consider the problem of the numerical approximation of the linear
controllability of waves. All our experiments are done in a bounded domain
\Omega of the plane, with Dirichlet boundary conditions and internal control.
We use a Galerkin approximation of the optimal control operator of the
continuous model, based on the spectral theory of the Laplace operator in
\Omega. This allows us to obtain surprisingly good illustrations of the main
theoretical results available on the controllability of waves, and to formulate
some questions for the future analysis of optimal control theory of waves.Comment: 38 figure
Greedy optimal control for elliptic problems and its application to turnpike problems
This is a post-peer-review, pre-copyedit version of an article published in Numerische Mathematik. The final authenticated version is available online at: https://doi.org/10.1007/s00211-018-1005-zWe adapt and apply greedy methods to approximate in an efficient way the optimal controls for parameterized elliptic control problems. Our results yield an optimal approximation procedure that, in particular, performs better than simply sampling the parameter-space to compute controls for each parameter value. The same method can be adapted for parabolic control problems, but this leads to greedy selections of the realizations of the parameters that depend on the initial datum under consideration. The turnpike property (which ensures that parabolic optimal control problems behave nearly in a static manner when the control horizon is long enough) allows using the elliptic greedy choice of the parameters in the parabolic setting too. We present various numerical experiments and an extensive discussion of the efficiency of our methodology for parabolic control and indicate a number of open problems arising when analyzing the convergence of the proposed algorithmsThis project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 694126-DyCon). Part of this research was done while the second author visited DeustoTech and Univesity of Deusto with the support of the DyCon project. The second author was also partially supported by Croatian Science Foundation under ConDyS Project, IP-2016-06-2468. The work of the third author was partially supported by the Grants MTM2014-52347, MTM2017-92996 of MINECO (Spain) and ICON of the French AN
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Optimal Control of Coupled Systems of PDE
The Workshop Optimal Control of Coupled Systems of PDE was held from April 17th – April 23rd, 2005 in the Mathematisches Forschungsinstitut Oberwolfach. The scientific program covered various topics such as controllability, feedback control, optimality conditions,analysis and control of Navier-Stokes equations, model reduction of large systems, optimal shape design, and applications in crystal growth, chemical reactions and aviation
The turnpike property in finite-dimensional nonlinear optimal control
Turnpike properties have been established long time ago in finite-dimensional
optimal control problems arising in econometry. They refer to the fact that,
under quite general assumptions, the optimal solutions of a given optimal
control problem settled in large time consist approximately of three pieces,
the first and the last of which being transient short-time arcs, and the middle
piece being a long-time arc staying exponentially close to the optimal
steady-state solution of an associated static optimal control problem. We
provide in this paper a general version of a turnpike theorem, valuable for
nonlinear dynamics without any specific assumption, and for very general
terminal conditions. Not only the optimal trajectory is shown to remain
exponentially close to a steady-state, but also the corresponding adjoint
vector of the Pontryagin maximum principle. The exponential closedness is
quantified with the use of appropriate normal forms of Riccati equations. We
show then how the property on the adjoint vector can be adequately used in
order to initialize successfully a numerical direct method, or a shooting
method. In particular, we provide an appropriate variant of the usual shooting
method in which we initialize the adjoint vector, not at the initial time, but
at the middle of the trajectory
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