After a brief introduction of the concepts of Distributional Jacobians, we will define a Mumford-Shah energy for vector valued maps that generalizes the classical one. We will then introduce a family of approximating energy and prove a Gamma-convergence result, in the spirit of the previous works by Ambrosio and Tortorelli.
In this talk, we will present some recent results about the asymptotic stability of rarefaction waves for the compressible isentropic Navier-Stokes equations with density-dependent viscosity. Both cases will be dicussed. One is that the rarefaction waves do not include vacuum. The other is that the rarefaction waves contact with vacuum. The theory holds for large-amplitudes rarefaction waves and arbitrary initial perturbations. This is joint with Yi Wang and Zhouping Xin.
We prove that the maximum norm of the deformation tensor of velocity gradients controls the possible breakdown of smooth(strong) solutions for the 3-dimensional compressible Navier-Stokes equations, which will happen, for example, if the initial density is compactly supported cite{X1}. More precisely, if a solution of the compressible Navier-Stokes equations is initially regular and loses its regularity at some later time, then the loss of regularity implies the growth without bound of the deformation tensor as the critical time approaches. Our result is the same as Ponce's criterion for 3-dimensional incompressible Euler equations (cite{po}). Moreover, our method can be generalized to the full Compressible Navier-Stokes system which improve the previous results. In addition, initial vacuum states are allowed in our cases.
This talk mainly concerns the mathematical justification of a viscous compressible multi-fluid model linked to the Baer-Nunziato model used by engineers, see for instance [M., Eyrolles (1975)]. More precisely, we show that some built approximate finite-energy weak solutions of the isentropic compressible Navier-Stokes equations converge, on a short time interval, to the strong solution of this viscous compressible multi-fluid model provided the initial density sequence is uniformly bounded with a corrresponding Young measure which is a linear convex combination of m Dirac measures.
It is well-known that one-dimensional isentropic gas dynamics has two elementary waves, i.e., shock wave and rarefaction wave. Among the two waves, only the rarefaction wave can be connected with vacuum. Given a rarefaction wave with one-side vacuum state to the compressible Euler equations, we can construct a sequence of solutions to one-dimensional compressible isentropic Navier-Stokes equations which converge to the above rarefaction wave with vacuum as the viscosity tends to zero. Moreover, the uniform convergence rate is obtained. The proof consists of a scaling argument and elementary energy analysis, based on the underlying rarefaction wave structures.
Compressed sensing (CS) is a new strategy to sample complicated data such as audio signals or natural images. Instead of performing a pointwise evaluation using localized sensors, signals are projected on a small number of delocalized random vectors. This talk is intended to give an overview of this emerging technology. It will cover both theoritical guarantees and practical applications in image processing and numerical analysis. The initial theory of CS was jointly developed by Donoho [1] and Candès, Romberg and Tao [2]. It makes use of the sparsity of signals to minimize the number of random measurements. Natural images are for instance well approximated using a few number of wavelets, and this sparsity is at the heart of the non-linear reconstruction process. I will discuss the extend to which the current theory captures the practical success of CS. I will pay a particular attention to the worse case analysis of the recovery, and perform a non-asymptotic evaluation of the performances [3]. To obtain better recovery guarantees, I propose a probabilistic analysis of the recovery of the sparsity support of the signal, which leads to constants that are explicit and small [4]. CS ideas have the potential to revolutionize other fields beyond signal processing. In particular, the resolution of large scale problems in numerical analysis could beneficiate from random projections. This performs a dimensionality reduction while simplifying the structure of the problem if the projection is well designed. As a proof of concept, I will present a new compressive wave equation solver, that use projections on random Laplacian eigenvectors [5]. [1] D. Donoho, Compressed sensing, IEEE Trans. Info. Theory, vol. 52, no. 4, pp. 1289-1306, 2006. [2] E. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Info. Theory, vol. 52, no. 2, pp. 489-509, 2006. [3] C. Dossal, G. Peyré and J. Fadili, A Numerical Exploration of Compressed Sampling Recovery, Linear Algebra and its Applications, Vol. 432(7), p.1663-1679, 2010. [4] C. Dossal, M.L. Chabanol, G. Peyré and J. Fadili, Sparse Support Identi
The ADER approach (Toro et al. 2001 and many others) allows the construction of non-linear one step fully discrete numerical schemes of arbitrary order of accuracy in space and time, for solving evolutionary partial differential equations. The ADER approach operates in the frameworks of finite volume and DG finite element methods and is applicable to multidimensional problems on unstructured meshes. The schemes have two basic ingredients: (a) a non-linear spatial reconstruction operator and (b) the solution of a generalized (or high-order) Riemann problem that links spatial data distribution and time evolution. After describing the main ideas of the methodology I will also show some applications involving hyperbolic and parabolic equations.
We extend the well-known Serrin's blowup criterion for the three-dimensional incompressible Navier-Stokes equations to the 3D compressible Navier-Stokes equations with vacuum. In other words, in addition to Serrin's condition on the velocity, the L^1(0,T;L^{infty}) norm of the divergence of the velocity is also needed to control the possible breakdown of strong (or smooth) solutions for the three-dimensional compressible Navier-Stokes equations. Moreover, under some additional constraint on the viscosity coefficients, either the L^1(0,T;L^{infty}) norm of the divergence of the velocity or the upper bound of the density will be enough to guarantee the global existence of classical (or strong) solutions.``
Nous présentons les méthodes d'optimisation de structure par la méthode des courbes de niveaux (level set). Nous montrons ensuite comment le modèle de Francfort-Marigo pour l'endommagement peut se traiter numériquement de façon efficace par ce type de méthode dès lors que l'on a calculé la dérivé de forme pour un problème à deux matériaux.
We consider the variational problem which consists in minimizing the compliance of a prescribed amount of elastic material, placed into a given design region, and sumbitted to an exterior balanced load. We discuss the asymptotic analysis of this problem when the design region is either a cylinder of infinitesimal height (case of thin plates) or a cylinder of infinitesimal cross section (case of thin rods). The results are contained in some recent papers in collaboration with Guy Bouchitte' and Pierre Seppecher.
Dans le cas des EDP stochastique, les solutions sont définies sur un espace de dimension infinie et les techniques utilisées pour des équations stochastiques ordinaires - fonction de Lyapunov, hypoellipticité, compacité du semi groupe de transition etc.- ne peuvent pas être appliquées ou nécessitent d'être adaptées. Dans cet exposé j'illustrerai des méthodes utilisées pour l'étude des mesures invariantes pour les EDP stochastiques et leurs applications à des cas spécifiques: dynamique de populations, équation de Burgers, équations de Navier-Stokes etc.
We present some numerical methods to solve control problems in the coefficients where the cost functional may depend on the gradient of the state non linearly. The main difficulty comes from the fact that the relaxed functional cost is not explicitly known. We prove some convergence results just using an upper or a lower approximation of this relaxed functional.
We consider a control problem in the coefficients for an elliptic linear equation where the cost functional is non-linear in the gradient of the function state. The control variables are the coefficients of the diffusion matrix. This type of problems arises in Optimal Design of Composite Materials. It is well known that they have not a solution in general. Here we use the homogenization method to obtain a relaxed formulation.