Section: Research Program
Range of inverse problems
Elliptic partial differential equations (PDE)
Participants : Laurent Baratchart, Sylvain Chevillard, Juliette Leblond, Konstantinos Mavreas, Christos Papageorgakis, Dmitry Ponomarev.
By standard properties of conjugate differentials, reconstructing DirichletNeumann boundary conditions for a function harmonic in a plane domain, when these conditions are already known on a subset $E$ of the boundary, is equivalent to recover a holomorphic function in the domain from its boundary values on $E$. This is the problem raised on the halfplane in step 1 of Section 3.1. It makes good sense in holomorphic Hardy spaces where functions are entirely determined by their values on boundary subsets of positive linear measure, which is the framework for Problem $\left(P\right)$ that we set up in Section 3.3.1. Such issues naturally arise in nondestructive testing of 2D (or 3D cylindrical) materials from partial electrical measurements on the boundary. For instance, the ratio between the tangential and the normal currents (the socalled Robin coefficient) tells one about corrosion of the material. Thus, solving Problem $\left(P\right)$ where $\psi $ is chosen to be the response of some uncorroded piece with identical shape yields non destructive testing of a potentially corroded piece of material, part of which is inaccessible to measurements. This was an initial application of holomorphic extremal problems to nondestructive control [54], [57].
Another application by the team deals with nonconstant conductivity over a doubly connected domain, the set $E$ being now the outer boundary. Measuring DirichletNeumann data on $E$, one wants to recover level lines of the solution to a conductivity equation, which is a socalled free boundary inverse problem. For this, given a closed curve inside the domain, we first quantify how constant the solution on this curve. To this effect, we state and solve an analog of Problem $\left(P\right)$, where the constraint bears on the real part of the function on the curve (it should be close to a constant there), in a Hardy space of a conjugate Beltrami equation, of which the considered conductivity equation is the compatibility condition (just like the Laplace equation is the compatibility condition of the CauchyRiemann system). Subsequently, a descent algorithm on the curve leads one to improve the initial guess. For example, when the domain is regarded as separating the edge of a tokamak's vessel from the plasma (rotational symmetry makes this a 2D situation), this method can be used to estimate the shape of a plasma subject to magnetic confinement. This was actually carried out in collaboration with CEA (French nuclear agency) and the University of Nice (JAD Lab.), to data from Tore Supra [60]. The procedure is fast because no numerical integration of the underlying PDE is needed, as an explicit basis of solutions to the conjugate Beltrami equation in terms of Bessel functions was found in this case. Generalizing this approach in a more systematic manner to free boundary problems of Bernoulli type, using descent algorithms based on shapegradient for such approximationtheoretic criteria, is an interesting prospect now under study in the team..
The piece of work we just mentioned requires defining and studying Hardy spaces of the conjugateBeltrami equation, which is an interesting topic by itself. For Sobolevsmooth coefficients of exponent greater than 2, they were investigated in [4], [34]. The case of the critical exponent 2 is treated in [12], which apparently provides the first example of wellposedness for the Dirichlet problem in the nonstrictly elliptic case: the conductivity may be unbounded or zero on sets of zero capacity and, accordingly, solutions need not be locally bounded. Exponent 2 seems also to be the key to a similar theory on general (rectifiable) domains in the plane, for exponent 2 is all one is left with in general after a conformal transformation of the domain.
Generalized Hardy classes as above are used in [13] where we address the uniqueness issue in the classical Robin inverse problem on a Lipschitz domain of $\Omega \subset {\mathbb{R}}^{n}$, $n\ge 2$, with uniformly bounded Robin coefficient, ${L}^{2}$ Neumann data and conductivity of Sobolev class ${W}^{1,r}\left(\Omega \right)$, $r>n$. We show that uniqueness of the Robin coefficient on a subset of the boundary, given Cauchy data on the complementary part, does hold in dimension $n=2$, thanks to a unique continuation result, but needs not hold in higher dimension. In higher dimension, this raises an open issue on harmonic gradients, namely whether the positivity of the Robin coefficient is compatible with identical vanishing of the boundary gradient on a subset of positive measure.
The 3D version of step 1 in Section 3.1 is another subject investigated by Apics: to recover a harmonic function (up to an additive constant) in a ball or a halfspace from partial knowledge of its gradient. This prototypical inverse problem (i.e. inverse to the Cauchy problem for the Laplace equation) often recurs in electromagnetism. At present, Apics is involved with solving instances of this inverse problem arising in two fields, namely medical imaging e.g. for electroencephalography (EEG) or magnetoencephalography (MEG), and paleomagnetism (recovery of rocks magnetization) [2], [36], see Section 5.1. In this connection, we collaborate with two groups of partners: Athena Inria projectteam, CHU La Timone, and BESA company on the one hand, Geosciences Lab. at MIT and Cerege CNRS Lab. on the other hand. The question is considerably more difficult than its 2D counterpart, due mainly to the lack of multiplicative structure for harmonic gradients. Still, substantial progress has been made over the last years using methods of harmonic analysis and operator theory.
The team is further concerned with 3D generalizations and applications to nondestructive control of step 2 in Section 3.1. A typical problem is here to localize inhomogeneities or defaults such as cracks, sources or occlusions in a planar or 3dimensional object, knowing thermal, electrical, or magnetic measurements on the boundary. These defaults can be expressed as a lack of harmonicity of the solution to the associated DirichletNeumann problem, thereby posing an inverse potential problem in order to recover them. In 2D, finding an optimal discretization of the potential in Sobolev norm amounts to solve a best rational approximation problem, and the question arises as to how the location of the singularities of the approximant (i.e. its poles) reflects the location of the singularities of the potential (i.e. the defaults we seek). This is a fairly deep issue in approximation theory, to which Apics contributed convergence results for certain classes of fields expressed as Cauchy integrals over extremal contours for the logarithmic potential [6], [37], [51]. Initial schemes to locate cracks or sources via rational approximation on planar domains were obtained this way [40], [44], [54]. It is remarkable that finite inverse source problems in 3D balls, or more general algebraic surfaces, can be approached using these 2D techniques upon slicing the domain into planar sections [7], [41]. More precisely, each section cuts out a planar domain, the boundary of which carries data which can be proved to match an algebraic function. The singularities of this algebraic function are not located at the 3D sources, but are related to them: the section contains a source if and only if some function of the singularities in that section meets a relative extremum. Using bisection it is thus possible to determine an extremal place along all sections parallel to a given plane direction, up to some threshold which has to be chosen small enough that one does not miss a source. This way, we reduce the original source problem in 3D to a sequence of inverse poles and branchpoints problems in 2D. This bottom line generates a steady research activity within Apics, and again applications are sought to medical imaging and geosciences, see Sections 4.3, 4.2 and 5.1.
Conjectures may be raised on the behavior of optimal potential discretization in 3D, but answering them is an ambitious program still in its infancy.
Systems, transfer and scattering
Participants : Laurent Baratchart, Matthias Caenepeel, Sylvain Chevillard, Martine Olivi, Fabien Seyfert.
Through contacts with CNES (French space agency), members of the team became involved in identification and tuning of microwave electromagnetic filters used in space telecommunications, see Section 4.4. The initial problem was to recover, from bandlimited frequency measurements, physical parameters of the device under examination. The latter consists of interconnected dualmode resonant cavities with negligible loss, hence its scattering matrix is modeled by a $2\times 2$ unitaryvalued matrix function on the frequency line, say the imaginary axis to fix ideas. In the bandwidth around the resonant frequency, a modal approximation of the Helmholtz equation in the cavities shows that this matrix is approximately rational, of McMillan degree twice the number of cavities.
This is where system theory comes into play, through the socalled realization process mapping a rational transfer function in the frequency domain to a statespace representation of the underlying system of linear differential equations in the time domain. Specifically, realizing the scattering matrix allows one to construct a virtual electrical network, equivalent to the filter, the parameters of which mediate in between the frequency response and the geometric characteristics of the cavities (i.e. the tuning parameters).
Hardy spaces provide a framework to transform this illposed issue into a series of regularized analytic and meromorphic approximation problems. More precisely, the procedure sketched in Section 3.1 goes as follows:

infer from the pointwise boundary data in the bandwidth a stable transfer function (i.e. one which is holomorphic in the right halfplane), that may be infinite dimensional (numerically: of high degree). This is done by solving a problem analogous to $\left(P\right)$ in Section 3.3.1, while taking into account prior knowledge on the decay of the response outside the bandwidth, see [9] for details.

A stable rational approximation of appropriate degree to the model obtained in the previous step is performed. For this, a descent method on the compact manifold of inner matrices of given size and degree is used, based on an original parametrization of stable transfer functions developed within the team [28], [9].

Realizations of this rational approximant are computed. To be useful, they must satisfy certain constraints imposed by the geometry of the device. These constraints typically come from the coupling topology of the equivalent electrical network used to model the filter. This network is composed of resonators, coupled according to some specific graph. This realization step can be recast, under appropriate compatibility conditions [55], as solving a zerodimensional multivariate polynomial system. To tackle this problem in practice, we use Gröbner basis techniques and continuation methods which team up in the DedaleHF software (see Section 3.4.1).
Let us mention that extensions of classical coupling matrix theory to frequencydependent (reactive) couplings have been carriedout in recent years [1] for wideband design applications.
Apics also investigates issues pertaining to design rather than identification. Given the topology of the filter, a basic problem in this connection is to find the optimal response subject to specifications that bear on rejection, transmission and group delay of the scattering parameters. Generalizing the classical approach based on Chebyshev polynomials for single band filters, we recast the problem of multiband response synthesis as a generalization of the classical Zolotarev minmax problem for rational functions [27] [8]. Thanks to quasiconvexity, the latter can be solved efficiently using iterative methods relying on linear programming. These were implemented in the software easyFF (see easyFF). Currently, the team is engaged in the synthesis of more complex microwave devices like multiplexers and routers, which connect several filters through wave guides. Schur analysis plays an important role here, because scattering matrices of passive systems are of Schur type (i.e. contractive in the stability region). The theory originates with the work of I. Schur [75], who devised a recursive test to check for contractivity of a holomorphic function in the disk. The socalled Schur parameters of a function may be viewed as Taylor coefficients for the hyperbolic metric of the disk, and the fact that Schur functions are contractions for that metric lies at the root of Schur's test. Generalizations thereof turn out to be efficient to parametrize solutions to contractive interpolation problems [29]. Dwelling on this, Apics contributed differential parametrizations (atlases of charts) of lossless matrix functions [28], [71], [65] which are fundamental to our rational approximation software RARL2 (see Section 3.4.4). Schur analysis is also instrumental to approach deembedding issues, and provides one with considerable insight into the socalled matching problem. The latter consists in maximizing the power a multiport can pass to a given load, and for reasons of efficiency it is allpervasive in microwave and electric network design, e.g. of antennas, multiplexers, wifi cards and more. It can be viewed as a rational approximation problem in the hyperbolic metric, and the team presently deals with this hot topic using contractive interpolation with constraints on boundary peak points, within the framework of the (defense funded) ANR COCORAM, see Sections 5.2 and 7.2.1.
In recent years, our attention was driven by CNES and UPV (Bilbao) to questions about stability of highfrequency amplifiers, see Section 6.2. Contrary to previously discussed devices, these are active components. The response of an amplifier can be linearized around a set of primary current and voltages, and then admittances of the corresponding electrical network can be computed at various frequencies, using the socalled harmonic balance method. The initial goal is to check for stability of the linearized model, so as to ascertain existence of a welldefined working state. The network is composed of lumped electrical elements namely inductors, capacitors, negative and positive reactors, transmission lines, and controlled current sources. Our research so far has focused on describing the algebraic structure of admittance functions, so as to set up a functiontheoretic framework where the twosteps approach outlined in Section 3.1 can be put to work. The main discovery is that the unstable part of each partial transfer function is rational and can be computed by analytic projection, see Section 5.4. We now start investigating the linearized harmonic transferfunction around a periodic cycle, to check for stability under non necessarily small inputs. This generalization generates both doctoral and postdoctoral work by new students in the team.