Séminaire de mathématiques appliquées (archives)

Hilbert, in his 1900 so-called "problems lecture", formulated as 6th problem the challenge to find an axiomatic basis for mechanics and probability. Kolmogorov's 1933 "Grundbegriffe" monograph was widely accepted as an adequate answer to this challenge regarding the axiomatisation of --- one has to say now --- "classical" probability. Coincidentally, 1900 is also the year when Planck formulated his thesis of energy quanta, which would give rise to quantum theory, and which requires a new probability theory. This became clear after Heisenberg's 1925 paper and his Göttingen colleagues' works afterwards, which together with Dirac introduced the algebraic point of view, culminating in von Neumann's 1932 monograph on the widely known Hilbert space representation of quantum mechanics; which actually appeared before Kolmogorov's monograph. The main difference between the classical and the "new" probability lay in the non-commutativity of random variables. Today there are also other areas where such quantum like behaviour (QLB) seems to occur.

The algebraic view offers a way on how to treat both classical and quantum like phenomena in a unified mathematical setting. And although probabilists today seem to be happy with Kolmogorov's approach based on measure theory, it may be interesting to look at the subject through a different pair of glasses. This algebraic view also offers a more direct way to address random variables with values in infinite dimensional spaces, something which with classical measure theory can only be done in a somewhat circumlocutory fashion. It also helps to separate purely algebraic questions from analytical ones, but of course thrives in the interplay of both.

Without wanting to present a strict axiomatic derivation, the start will be an early --- and in the light of modern theory also abstract algebraic --- view on random variables, as can be found implicitly in the work of early probabilists like the Bernoullis. Their properties are sketched as emanating from simple operational requirements regarding random variables, the mean or expectation, as well as sampling or observations. Concrete representations of this abstract setting connect it with algebras of linear mappings and the spectral theory of these, and one may recover Kolmogorov's classical characterisation as one particular representation.

Striking differences between classical or commutative probability and non-commutative probability appear already with simple linear algebra. As this is a subject which nowadays all engineering and science students learn at a very early stage, it may also be an interesting approach to teaching probability. And possible novel devices like quantum computers can be described in this setting.

This algebraic view has also a functional analytic extension, which can be used to construct generalised random variables and "ideal elements". It allows the specification of not only analogues of all the classical spaces of random variables, but to go beyond this and address questions of "smoothness" on the one hand, and the definition of idealised elements resp. "generalised" random variables on the other hand. This very much echoes the construction of distributions resp. generalised functions in the sense of Sobolev and Schwartz.

The method of moments is commonly used to reduce a kinetic equation into a fluid model. In this talk, I will present this technique as a semi-discretization with respect to the kinetic variable. I will focus on the main properties expected for this approximation, namely the positivity of an underlying kinetic approximation, a.k.a. the realizability, the strong or weak hyperbolicity and the entropy dissipation of the resulting system. I will present some novelties around these approximations, classified in three categories: the quadrature-based methods, the entropy-based methods and the realizability-based methods. Eventually, I will give some ideas on how to analyze such approximations and illustrate it on some kinetic toy problems.

In this paper, we study the asymptotic behavior of the global solution to a degenerate forest kinematic model, under the action of a perturbation modelling the impact of climate change. When the main nonlinearity of the model is assumed to be monotone, we prove that the global solution converges to a stationary solution, by showing that a Lyapunov function deduced from the system satisfies a Lojasiewicz-Simon gradient inequality. Under suitable assumptions on the parameters, we prove the continuity of the flow and of the stationary solutions with respect to the perturbation parameter. Although, due to a lack of compactness, the system does not admit the global attractor, we succeed in proving the robustness of the weak attractors, by establishing the existence of a family of positively invariant regions. We also present numerical simulations of the model and experiment the behavior of the solution under the effect of several types of perturbations. Finally, we show that the forest kinematic model can lead to the emergence of chaotic patterns

We propose a natural framework for the study of surfaces and their different discretizations based on varifolds. Varifolds have been introduced by Almgren to carry out the study of minimal surfaces. Though mainly used in the context of rectifiable sets, they turn out to be well suited to the study of discrete type objects as well. While the structure of varifold is flexible enough to adapt to both regular and discrete objects, it allows to define variational notions of mean curvature and second fundamental form based on the divergence theorem. Thanks to a regularization of these weak formulations, we propose a notion of discrete curvature (actually a family of discrete curvatures associated with a regularization scale) relying only on the varifold structure. We performed numerical computations of mean curvature and Gaussian curvature on point clouds in R^3 to illustrate this approach. Though flexible, varifolds require the knowledge of the dimension of the shape to be considered. By interpreting the product of the Principal Component Analysis, that is the covariance matrix, as a sequence of nested subspaces naturally coming with weights according to the level of approximation they provide, we are able to embed all d-dimensional Grassmannians into a stratified space of covariance matrices. Building upon the proposed embedding of Grassmannians into the space of covariance matrices, we generalize the concept of varifolds to what we call flagfolds in order to model multi-dimensional shapes.

Joint work with: Gian Paolo Leonardi (Trento), Simon Masnou (Lyon) and Xavier Pennec (INRIA Sophia).

L'objectif du travail présenté est l'étude de séries temporelles radar qui sont par nature des séries temporelles complexes centrées. Dans la première partie de cette présentation, nous souhaitons réaliser le clustering de fouillis radar, c'est-à-dire des données radar liées à l'environnement tels les mers, les forêts ou les champs environnants. Nous supposerons que les séries temporelles complexes observées suivent un modèle autorégressif gaussien stationnaire centré. De telles séries temporelles peuvent être représentées par leurs matrices de covariance qui sont des matrices Toeplitz hermitiennes définies positives. Elles peuvent également être représentées par les coefficients du modèle autorégressif. Certains coefficients autorégressifs appelés coefficients de réflexion sont de module strictement inférieur à 1 et déterminent entièrement le modèle autorégressif. Nous munirons cet espace de représentation d'une métrique riemannienne inspirée de la métrique de la géométrie de l'information sur les matrices hermitiennes définies positives. La variété riemannienne obtenue est une variété produit faisant intervenir plusieurs disques de Poincaré (autant que l'ordre du modèle autorégressif). Nous utiliserons alors l'algorithme des k-means dans cette variété riemannienne pour réaliser le clustering de fouillis radar. Dans la seconde partie de cette présentation, nous chercherons à faire de la détection et de la classification de drones à partir de séries temporelles radar. Les séries temporelles associées aux drones ne sont pas stationnaires, elles se distinguent par l'effet micro-Doppler induit par la rotation des hélices. Les séries temporelles complexes seront alors segmentées en fenêtres d'observation plus courtes. Pour chaque fenêtre, nous supposerons que la courte série temporelle observée est stationnaire et nous la représenterons par un point dans la variété riemannienne présentée dans la première partie. Nous obtiendrons alors une série temporelle riemannienne dont nous exploiterons les caractéristiques géométriques et statistiques pour faire de la détection et de la classification de drones.